Rwanda Baseline Soil Health Study Report

1 Objective

Summary of the demographics and soil characteristics of the Rwanda long term soil health study.

1.1 Study methodology

Add in details and links on study methodology here.

library(knitr)
library(ggplot2)
library(stringr)
suppressMessages(library(dplyr))
library(sp)
suppressMessages(library(rgdal))
suppressMessages(library(dismo))
suppressMessages(library(stargazer))
library(leaflet)
library(XML)
suppressMessages(library(maptools))
library(automap)
suppressMessages(library(RStata))
suppressMessages(library(fields))
library(gstat)
library(htmltools)
suppressMessages(library(Matching))
library(reshape2)
options("RStata.StataVersion" = 12)
options("RStata.StataPath" = "/Applications/Stata/StataSE.app/Contents/MacOS/stata-se")
#chooseStataBin("/Applications/Stata/StataSE.app/Contents/MacOS/stata-se")

The working directory objects are from the original baseline analysis. I’ve updated this to pull from a stable working directory (5/4/17)

# wd <- "/Users/mlowes/drive/optimized_agronomy/soil/soil_health_study/data/rw baseline"
# dd <- paste(wd, "data", sep = "/")
# od <- paste(wd, "output", sep="/")
# md <- paste(wd, "maps", sep="/")
drive <- "~/drive/r_help/4_output/statistical_test_outputs"
od <- "output" # come back and update this later. I got about half way through?
#load data:
# This data is being drawn from the Soil lab repository. It has the baseline data with it
# d <- read.csv(paste(dd, "Rwanda_shs_commcare_soil_data_final.csv", sep="/"),  stringsAsFactors=FALSE)

2 Data Prep

2.1 Combine survey and soil data

Replicate Alex’s and Emmanuel’s merge process using “Identifiers with SSN_final” provided by Emmanuel. I use the RStata package found here

# I'm replicating Alex's do file here.
stata("merge_shs.do")

. * merge raw commcare data with soil database
. 
. * date: 11 july 2016
. 
. clear all
. set more off
. 
. * set directory
. global wd "/Users/mlowes/drive/soil health study/data/rw baseline"
. global dd "/Users/mlowes/drive/soil health study/data/rw baseline/data"
. global troubleshoot "/Users/mlowes/drive/soil health study/data/rw baseline/t
> roubleshoot"
. 
. * insheet data
. insheet using "$dd/raw_rwanda_commcare_shs.csv", clear
file /Users/mlowes/drive/soil health study/data/rw
    baseline/data/raw_rwanda_commcare_shs.csv not found
r(601);
. 
. * clean sample_id variable
. replace sample_id = "" if sample_id == "---"
. replace sample_id = "-99" if sample_id == "99"
. 
. destring sample_id, replace
. 
. * manipulate current "sample_id" to become a proper alpha-numeric unique iden
> tifier
. * this simply involves adding "C" to each numeric code that belongs to a cont
> rol farmer
. 
. * variables of interest:
.       * d_client
.       * sample_id
. 
. ren demographic_infod_client d_client
. replace d_client = "" if d_client == "---"
. destring d_client, replace
. 
. tostring sample_id, gen(sample_id_string)
. 
. replace sample_id_string = sample_id_string + "C" if d_client == 0
. drop sample_id 
. ren sample_id_string sample_id
. 
. ***** !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ***
> ***
. * quantify and note cases where sample_id appears more than twice
.      * 39 codes appear 2x
.      * 1 code appears 3x
.      * 1 code appears 4x
.      * 1 code appears 10x
.      * 1 code appears 19x
. 
. duplicates report sample_id
. duplicates tag sample_id, gen(n_duplicate_id)
. gen d_id_problem = 1 if n_duplicate_id != 0
. replace d_id_problem = 0 if missing(d_id_problem)
. 
. ** need to investigate 5% of observations (114 samples), i.e. d_id_problem = 
> 1**
. ***** !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ***
> ***
. 
. * outsheet problematic observations and examine 1-by-1
. sort sample_id
. outsheet demographicnom_enqueteur district demographic_infocellule_selected d
> emographic_infoumudugudu d_client demographic_infonom_cultivateur sample_id d
> emographic_infosex demographic_infoage_cultivateur demographic_infon_telephon
> e_cult d_id_problem using "$dd/shs_to_check.csv" if d_id_problem == 1, replac
> e comma
. 
. **************** post-investigation correction of incorrect ids *************
> ***
. 
. ren demographic_infonom_cultivateur farmer_name
. ren demographic_infocellule_selected selected_cell
. ren demographicnom_enqueteur enumerator_name
. 
. * drop test/accidentally double-entered observations
. drop if infoformid == "f138897f-6e80-4b0a-9db3-ab2134cd9e51"
. drop if farmer_name == "Mugisha"
. drop if farmer_name == "Muhunde yoramu"
. drop if farmer_name == "Test"
. drop if farmer_name == "Billy"
. drop if farmer_name == "Gakuba michel"
. drop if farmer_name == "Renata"
. drop if farmer_name == "Bub"
. drop if farmer_name == "M"
. drop if farmer_name == "Anita"
. drop if farmer_name == "Jean"
. drop if farmer_name == "Nyirazaninka lea"
. drop if farmer_name == "Mugiraneza Pacifiue"
. drop if farmer_name == "Nsengiyaremye Innocent"
. 
. * correct incorrectly recorded ids
. replace sample_id = "880C" if farmer_name == "Nyiransanzineza Marie Rose"
. replace sample_id = "811C" if farmer_name == "Ngirabatware  Daniel"
. replace sample_id = "646C" if farmer_name == "Nyirasabwa anonciata"
. replace sample_id = "624C" if farmer_name == "Nyirinkindi j Bosco"
. replace sample_id = "560C" if farmer_name == "Mubirigi Bertin"
. replace d_client = 0 if farmer_name == "Mubirigi Bertin"
. replace sample_id = "375C" if farmer_name == "Hitimana ezecquiere"
. replace sample_id = "322C" if farmer_name == "Nyirantabire arrivera"
. replace sample_id = "2566C" if farmer_name == "MUSABYIMANA Colletta"
. replace sample_id = "2399C" if farmer_name == "Nyirabajyambere anastisia"
. replace d_client = 0 if farmer_name == "Nyirabajyambere anastisia"
. replace sample_id = "2280C" if farmer_name == "Zihabake Simon"
. replace sample_id = "2202C" if farmer_name == "mugiraneza pacifique"
. replace sample_id = "1780C" if farmer_name == "Hakuzimana Theophile"
. replace sample_id = "1757C" if farmer_name == "Bavugirije Feleciane"
. replace sample_id = "1626C" if farmer_name == "Nyabivumu"
. replace sample_id = "1575C" if farmer_name == "BAHIMBANDE Beriyana."
. replace d_client = 0 if farmer_name == "BAHIMBANDE Beriyana."
. replace sample_id = "1103C" if farmer_name == "Barirwanda Elyse"
. replace sample_id = "1037C" if farmer_name == "Mukashema Faraziya"
. replace d_client = 0 if farmer_name == "Mukashema Faraziya" 
. replace sample_id = "3069" if farmer_name == "Ndababonye Silas"
. replace sample_id = "2611" if farmer_name == "Sebazungu modeste"
. replace d_client = 1 if farmer_name == "Sebazungu modeste"
. replace sample_id = "2415" if farmer_name == "Nkaka joseph"
. replace sample_id = "2412" if farmer_name == "Ndagijimana alexis"
. replace sample_id = "2410" if farmer_name == "Sebuhoro elie"
. replace sample_id = "2406" if farmer_name == "Mugisha ruth"
. replace sample_id = "2405" if farmer_name == "Mboneza hasan"
. replace sample_id = "2404" if farmer_name == "Ntibitonderwa veriane"
. replace sample_id = "2399" if farmer_name == "Nyiraburakeye feresita"
. replace sample_id = "2395" if farmer_name == "Uwimana danier"
. replace sample_id = "2394" if farmer_name == "Bizinde silas"
. replace sample_id = "2390" if farmer_name == "Nyirahabimvana keresesiya"
. replace sample_id = "2388" if farmer_name == "Nyirasekerabanzi tharcilla"
. replace sample_id = "2386" if farmer_name == "Ndererimana emmanuel"
. replace sample_id = "2291" if farmer_name == "Ndacyayisenga Feresiyani"
. replace sample_id = "2184" if farmer_name == "Muhawenimana Beretirida"
. replace sample_id = "2145" if farmer_name == "Fapfakwita celestine"
. replace sample_id = "2141" if farmer_name == "Nsangiranabo krizoroji"
. replace sample_id = "2140" if farmer_name == "Nsabyumuremyi anakereti"
. replace sample_id = "2136" if farmer_name == "Nyirantibitonderwa savoroniya"
. replace sample_id = "2131" if farmer_name == "Hagenimana Ewufroni"
. replace sample_id = "2066" if farmer_name == "Hitayezu vicent"
. replace d_client = 1 if farmer_name == "Hitayezu vicent"
. replace sample_id = "2044" if farmer_name == "Niyodushima janette"
. replace d_client = 1 if farmer_name == "Niyodushima janette"
. replace sample_id = "2024" if farmer_name == "Mukabatsinda veren A"
. replace sample_id = "2020" if farmer_name == "Sibomana innocent"
. replace sample_id = "2009" if farmer_name == "Nsabimana inyasi"
. replace sample_id = "2002" if farmer_name == "Mateso elizabet"
. replace sample_id = "1889" if farmer_name == "Niyonsaba Daniel"
. replace sample_id = "1864" if farmer_name == "Mukankubana Souzanne"
. replace sample_id = "1780" if farmer_name == "Nyirantihabose Annonciatha"
. replace sample_id = "1771" if farmer_name == "Bizimungu Damascne"
. replace sample_id = "1675" if farmer_name == "Bikorimana Isaac"
. replace sample_id = "1103" if farmer_name == "Munyabarata japhette"
. replace sample_id = "954" if farmer_name == "Hahirwabake silver"
. replace sample_id = "541" if farmer_name == "Rugemintwaza Vianney"
. replace sample_id = "454" if farmer_name == "Niyonizeye fororida"
. replace sample_id = "407" if farmer_name == "Munyaneza jean pierre"
. replace sample_id = "375" if farmer_name == "Munyarubuga nanias"
. replace sample_id = "322" if farmer_name == "Gacandaga zackarie"
. replace sample_id = "262" if farmer_name == "MUHIRWA J Damascene"
. replace sample_id = "32" if farmer_name == "Nsanzemuhire"
. 
. *** re-run duplicates test
. drop d_id_problem
. drop n_duplicate_id
. 
. duplicates report sample_id
. duplicates tag sample_id, gen(n_duplicate_id)
. gen d_id_problem = 1 if n_duplicate_id != 0
. replace d_id_problem = 0 if missing(d_id_problem)
. 
. * second try: outsheet problematic observations and examine 1-by-1
. sort sample_id
. outsheet using "$dd/shs_to_check_2.csv" if d_id_problem == 1, replace comma
. 
. * second try: correct incorrect ids
. 
. replace sample_id = "2202" if farmer_name == "Mukamurara scholastique"
. replace d_client = 1 if farmer_name == "Mukamurara scholastique"
. replace sample_id = "2195" if farmer_name == "Ndacyayisenga Feresiyani"
. replace sample_id = "824" if farmer_name == "Ngirabatware  Daniel"
. 
. replace sample_id = "" if rownumber == 238
. replace sample_id = "" if rownumber == 848
. replace sample_id = "" if rownumber == 1257
. replace sample_id = "" if rownumber == 1732
. 
. *** re-run duplicates test
. drop d_id_problem
. drop n_duplicate_id
. 
. duplicates report sample_id
. duplicates tag sample_id, gen(n_duplicate_id)
. gen d_id_problem = 1 if n_duplicate_id != 0
. replace d_id_problem = 0 if missing(d_id_problem)
. 
. * third try: outsheet problematic observations
. sort sample_id
. outsheet using "$dd/shs_to_check_3.csv" if d_id_problem == 1, replace comma
. 
. *** 4 sets of duplicates remain (2 observations)
. drop if d_id_problem == 1
. save "$dd/rwanda_commcare_shs_clean.dta", replace
. outsheet using "$dd/rwanda_commcare_shs_clean.csv", comma replace
. 
. * merge datasets
. insheet using "$dd/mne_rwanda_database_shs.csv", clear
. 
. save "$dd/mne_rwanda_database_shs.dta", replace
. 
. use "$dd/rwanda_commcare_shs_clean.dta", clear
. 
. merge 1:1 sample_id using "$dd/mne_rwanda_database_shs.dta"
. 
.    * 93 observations unmatched... :(
. 
. * investigate unmatched observations
. sort sample_id
. outsheet using "$troubleshoot/unmatched_commcare.csv" if _merge == 1, comma r
> eplace
. outsheet using "$troubleshoot/unmatched_inventory.csv" if _merge == 2, comma 
> replace
. 
. * make first round of corrections in master data
. use "$dd/rwanda_commcare_shs_clean.dta", clear
. 
. replace sample_id = "2711C" if farmer_name == "Kamuhanda Elie"
. replace sample_id = "260C" if farmer_name == "HATEGEKIMANA Mathias"
. replace sample_id = "245" if farmer_name == "Kayiranga Michel"
. replace sample_id = "1543" if farmer_name == "Ndagijimana Eliezel"
. replace sample_id = "1543C" if farmer_name == "Mukantanganzwa odeta"
. replace sample_id = "1632" if farmer_name == "Nyiranziguye Cecile"
. replace sample_id = "2782" if farmer_name == "Mukamungu leocadie"
. replace sample_id = "421C" if farmer_name == "Niyonizeye Francine"
. replace sample_id = "828C" if farmer_name == "Mukamazimpaka Terese"
. replace sample_id = "364" if farmer_name == "Mukamuyango verina"
. replace sample_id = "364C" if farmer_name == "Mujawimana jeanne"
. replace sample_id = "368" if farmer_name == "Nyiramanzi jean damascene"
. replace sample_id = "368C" if farmer_name == "Karwiyegura rachel"
. replace sample_id = "391" if farmer_name == "Mugemane augistin"
. replace sample_id = "391C" if farmer_name == "Ngirishuti sumayire"
. replace sample_id = "395" if farmer_name == "Niyomugabo amiel"
. replace sample_id = "395C" if farmer_name == "Ntawiniga augustin"
. replace sample_id = "396" if farmer_name == "Mpayimana philippe"
. replace sample_id = "329" if farmer_name == "Mucyezangango emmanuel"
. replace sample_id = "411" if farmer_name == "Nsabimana mathias"
. replace sample_id = "411C" if farmer_name == "Singirankabo"
. replace sample_id = "414" if farmer_name == "Rutaharama eldephonse"
. replace sample_id = "329C" if farmer_name == "Mukamana josephine"
. replace sample_id = "657" if farmer_name == "Nyandwi vincent"
. replace sample_id = "569C" if farmer_name == "Uwimana Bercilla"
. replace sample_id = "2051C" if farmer_name == "Ntambabazi Anastase"
. replace sample_id = "2249" if farmer_name == "Uwineza valentine"
. replace sample_id = "2360C" if farmer_name == "Bamporineza j deDieu"
. replace sample_id = "2001" if farmer_name == "Nyiramakuba thanene"
. replace sample_id = "2897C" if farmer_name == "Mukagatanazi Marianne"
. replace sample_id = "2965C" if farmer_name == "Twizeyimana Theoneste"
. 
. drop if farmer_name == "Yjk"
. drop if farmer_name == "Uwambajimana Agnes"
. drop if farmer_name == "Tr"
. 
. replace sample_id = "2415C" if farmer_name == "Nyiramahirwe Frolance"
. replace sample_id = "851" if farmer_name == "Bigirimana  Amon"
. 
. 
. * duplicates check
. drop d_id_problem
. drop n_duplicate_id
. 
. duplicates report sample_id
. duplicates tag sample_id, gen(n_duplicate_id)
. gen d_id_problem = 1 if n_duplicate_id != 0
. replace d_id_problem = 0 if missing(d_id_problem)
. 
. * second try: merge
. merge 1:1 sample_id using "$dd/mne_rwanda_database_shs.dta"
. 
. * deal with 30 unmatched cases
. sort sample_id
. outsheet using "$troubleshoot/unmatched_commcare_2.csv" if _merge == 1, comma
>  replace
. outsheet using "$troubleshoot/unmatched_inventory_2.csv" if _merge == 2, comm
> a replace
. 
. * 5 submitted surveys with no corresponding sample
. *drop if _merge == 1
. 
. * 19 samples with no corresponding survey
. *drop if _merge == 2
. 
. * outsheet merged database
. outsheet using "$dd/rwanda_shs_baseline_data.csv", comma replace
. * THIS IS THE CLEAN VERSION! ONLY USE THIS! DON'T USE OTHER THINGS!
. 

Import the results of Alex’s do file.

d <- read.csv("data/rwanda_shs_baseline_data.csv", stringsAsFactors = F)
Identifiers <- read.csv("data/Identifiers with SSN_final.csv", stringsAsFactors = F)
wetChem <- read.csv(paste("/Users/mlowes/drive/jupyter/Rwanda Acidity", "Original chem_rwanda_shs.csv", sep = "/"))

d <- left_join(d, Identifiers, by="sample_id")
# now import the soil results and merge with survey data
soilDir <- normalizePath(file.path("..", "..", "OAF Soil Lab Folder", "Projects", "rw_shs_baseline", "4_predicted", "other_summaries"))
soilRF <- read.csv(file=paste(soilDir, "combined-predictions-including-bad-ones.csv", sep="/"))
orgSoilNames <- read.csv("data/rwshs_rfresults.csv", stringsAsFactors = F) #old from original variable work
names(orgSoilNames)[names(orgSoilNames)=="X"] <- "SSN"

Match new soil model outputs to variable names from original analysis in 2016 and join soil variables with survey data

soil <- soilRF %>% dplyr::select(SSN, Calcium, Magnesium, pH, Total.Nitrogen, Organic.Carbon, Exchangeable.Aluminium) %>%
  rename(
    m3.Ca = Calcium,
    m3.Mg = Magnesium,
    Total.N = Total.Nitrogen,
    Total.C = Organic.Carbon,
    ExAl = Exchangeable.Aluminium
  ) %>% mutate(
    SSN = as.character(SSN)
  )
d <- left_join(d, soil, by="SSN")

2.2 Cleaning baseline variables

Now let’s start cleaning the demographic variables

d[d=="---"] <- NA
names(d) <- gsub("text_final_questions", "", names(d))
names(d) <- gsub("intro_champ_echantillon", "", names(d))
names(d) <- gsub("demographic_info", "", names(d))
names(d) <- gsub("other_inputs_", "", names(d))
names(d) <- gsub("crop1_15b_inputs", "", names(d))
names(d) <- gsub("crop2_15b_inputs", "", names(d))
names(d) <- gsub("^15b", "", names(d))
names(d) <- gsub("historical_intro", "", names(d))
names(d)[names(d)=="field_dim"] <- "field_dim1"
names(d)[names(d)=="v51"] <- "field_dim2"

Address unusual field sizes

ggplot(d, aes(x=field_dim1, y=field_dim2)) + geom_point()

It seems really unlikely that fields are 200 meters long while only being 20 meters wide. I don’t know how to check this though.

# clean field dimensions here - winsor the values to something reasonable.
d[(d$field_dim1>=100 | d$field_dim2>=100) & !is.na(d$field_dim1), c("field_dim1", "field_dim2")]

Take care of demographic data formatting issues

# deal with names and drop unnecessary variables
d <- d %>% 
    dplyr::select(-c(rownumber, infoformid, introductiond_accept, photo,
        infocompleted_time, 
        enumerator_name, contains("phone"), farmer_name, farmersurname, farmername,
        d_respondent, additionalsamplepackedandsenttol, additionalsamplerequestedfromlab,
        datedryingcompleteifnecessary, driedindistrictifnecessary, senttohqyo,
        collectedindistrictyo, excessstoredathq_, receivedathq_,dateofinitialdryingifnecessary,
        samplecollectedinfieldyo, field_des, samplewetordry)) %>%
    rename(
    female = sex,
    age = age_cultivateur,
    own = d_own,
    client = d_client) %>%
    mutate(
    female= ifelse(female=="gore", 1,0),
    field.size = field_dim1*field_dim2
    )
d$total.seasons <- apply(d[, grep("d_season_list", names(d))], 1, function(x) {
    sum(x, na.rm=T)})

Clean agronomic practice variables

agVars <- c("n_season_fert", "n_season_compost", "n_season_lime", "n_season_fallow",
            "n_seasons_leg_1", "n_seasons_leg_2")
summary(d[,agVars])

 n_season_fert    n_season_compost n_season_lime     n_season_fallow   n_seasons_leg_1  n_seasons_leg_2   
 Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : -88.000  
 1st Qu.: 0.000   1st Qu.: 2.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.:   0.000  
 Median : 1.000   Median : 5.000   Median : 0.0000   Median : 0.0000   Median : 1.000   Median :   2.000  
 Mean   : 2.041   Mean   : 5.651   Mean   : 0.1819   Mean   : 0.6355   Mean   : 2.171   Mean   :   4.368  
 3rd Qu.: 3.000   3rd Qu.:10.000   3rd Qu.: 0.0000   3rd Qu.: 0.0000   3rd Qu.: 4.000   3rd Qu.:   5.000  
 Max.   :10.000   Max.   :10.000   Max.   :10.0000   Max.   :10.0000   Max.   :10.000   Max.   :3333.000  
 NA's   :19       NA's   :19       NA's   :19        NA's   :19        NA's   :19       NA's   :19        

Sort out the legumes as a second crop

table(d$n_seasons_leg_2, useNA = 'ifany')

 -88    0    1    2    3    4    5    6    7    8    9   10   15   22   50   53   83   88  101  102 3333 <NA> 
   1  956  221  258  174  130  301   61   34   66   57  199    1    1    1    3    1    1    1    1    1   19 

d$n_seasons_leg_2 <- ifelse(d$n_seasons_leg_2 <0 | d$n_seasons_leg_2>10, NA, d$n_seasons_leg_2)

2.2.1 Check out client variable - why are there missing values?

table(d$client, useNA = 'ifany')

   0    1 <NA> 
1233 1236   19 

d[is.na(d$client), c("sample_id")]

 [1] "1189C" "1207C" "1295"  "1295C" "1298C" "1300"  "1300C" "1366C" "1476C" "1485C" "1554"  "1573"  "1614"  "2042"  "2371C" "2373"  "2375" 
[18] "2382"  "2442" 

# replace client based on whether there is a C in the client variable.
d$client.check <- ifelse(grepl("C", d$sample_id)==T, 0, 1)
table(d$client, d$client.check)

   
       0    1
  0 1233    0
  1    1 1235

It looks like most farmers were recorded correctly except for one farmer who was coded as Tubura farmer but their sample_id indicate their a control. Let’s take a look:

d[d$client==1 & d$client.check==0 & !is.na(d$d_gps), c("sample_id", "tuburacontroltc")]

# they should be a control
d[d$client==1 & d$client.check==0 & !is.na(d$d_gps), "client"] <- 0
# remove farmers for which we have soil data but no survey data (using)
d <- d[-which(grepl("using", d$X_merge)),]
table(d$client, d$client.check, useNA = 'ifany')

   
       0    1
  0 1234    0
  1    0 1235

# update client to equal client check
d$client <- d$client.check

Fix some more variable names:

names(d)[names(d)=="field_kg_fert1_1"] <- "field_kg_fert1_15b"
names(d)[names(d)=="field_kg_fert2_1"] <- "field_kg_fert2_15b"
names(d)[names(d)=="field_kg_compost"] <- "field_kg_compost_15b"

Recode variables to numeric:

# recode to numeric
varlist <- c("client", "own", "crop1_15b_seedkg", "crop1_15b_yield", "crop1_15b_yield_",
    "crop2_15b_seedkg", "crop2_15b_yield", "crop2_15b_yield_", "field_kg_fert1_15b",
    "field_kg_fert2_15b", "field_kg_compost_15b", "d_lime_15b", "kg_lime_15b")
# check that there aren't values hidden in the character variables
#apply(d[,varlist], 2, function(x){table(x, useNA='ifany')})
# recode characters to numerics
d[, varlist] <- sapply(d[,varlist], as.numeric)

2.2.2 Sort out kgs of lime applied

table(d$kg_lime_15b, useNA = 'ifany')

 -88    1    3    4    5   10   13   15   20   25   30   35   50   60   88  100  150  200 <NA> 
   1    2    2    2    6    5    1    1    2    5    1    1    8    1    2    2    4    1 2422 

d$kg_lime_15b <- ifelse(abs(d$kg_lime_15b)==88, NA, d$kg_lime_15b)

# divide out GPS coordinates
# http://rfunction.com/archives/1499
# replace the blank gps_pic_guide with info
d <- cbind(d, str_split_fixed(d$gps_pic_guid, " ", n=4))
names(d)[names(d)=="1" |names(d)== "2" | names(d)== "3" | names(d)== "4"] <- c("lat", "lon", "alt", "precision")
d[,c("lat", "lon", "alt", "precision")] <- sapply(d[,c("lat", "lon", "alt", "precision")],
                                                  function(x){as.numeric(as.character(x))})

2.3 Cleaning soil data

Cleaning of soil data: Come back, check and clean the soil data before outputting to clean data set. Plot each of the soil variables to look for unrealistic values.

dim(d[is.na(d$m3.Ca),])

[1]  30 111

d <- d[-which(is.na(d$m3.Ca)),]
       
summary(d[,c("m3.Ca", "m3.Mg", "pH", "Total.N", "Total.C", "ExAl")])

     m3.Ca            m3.Mg               pH           Total.N          Total.C            ExAl        
 Min.   :  90.8   Min.   :  39.45   Min.   :3.840   Min.   :0.0600   Min.   :0.8749   Min.   :0.01578  
 1st Qu.: 358.0   1st Qu.: 121.32   1st Qu.:4.995   1st Qu.:0.1300   1st Qu.:1.7290   1st Qu.:0.13532  
 Median : 620.5   Median : 184.74   Median :5.470   Median :0.1500   Median :2.0301   Median :0.30884  
 Mean   : 821.3   Mean   : 209.82   Mean   :5.483   Mean   :0.1521   Mean   :2.0791   Mean   :0.59145  
 3rd Qu.:1102.5   3rd Qu.: 262.09   3rd Qu.:5.950   3rd Qu.:0.1700   3rd Qu.:2.3466   3rd Qu.:0.93700  
 Max.   :6090.4   Max.   :1052.13   Max.   :8.140   Max.   :0.3500   Max.   :4.0658   Max.   :2.50849  

2.3.1 Soil Type

names(d)[names(d)=="general_field_infosoil_type"] <- "soil_type"
names(d)[names(d)=="general_field_infotexture_soil"] <- "soil_texture"
d$black_soil <- ifelse(d$soil_type=="black", 1,0)
d$red_soil <- ifelse(d$soil_type=="red", 1,0)
d$sandy_soil <-ifelse(d$soil_type=="sandy", 1,0)

Let’s check out the rows for which we don’t have soil data and drop them as they won’t contribute to the full picture.

2.3.2 Graphs of RW baseline soil variables

soilVars <- c("m3.Ca", "m3.Mg", "pH", "Total.N", "Total.C", "ExAl")

for(i in 1:length(soilVars)){
print(
  ggplot(data=d, aes(x=as.factor(client), y=d[,soilVars[i]])) + 
    geom_boxplot() +
    labs(x="Tubura Farmer", y=soilVars[i], title = paste("RW baseline soil - ", soilVars[i], sep = ""))
  )  
}

2.3.3 Check out soil relationships

There are biologically predictable relationships between soil chemical characteristics. For instance, we expect Ca and Mg to move in the same direction and be positively correlated with pH. If we had Aluminum as an outcome, we’d expect pH to be negatively correlated with soluable aluminum. Let’s look quickly to confirm if those relationships are present:

ggplot(d, aes(x=m3.Ca, y=m3.Mg)) + geom_point() +
    stat_smooth(method="loess") +
    labs(x = "Calcium (m3)", y= "Magnesium (m3)", title="Calcium and Magnesium relationship")

ggplot(d, aes(x=pH, y=m3.Ca)) + geom_point() +
  stat_smooth(method="loess") +
  labs(x = "pH", y="Calcium (m3)", title = "pH and Calcium relationship")

ggplot(d, aes(x=pH, y=m3.Mg)) + geom_point() +
  stat_smooth(method="loess") +
  labs(x = "pH", y="Magnesium (m3)", title = "pH and Magnesium relationship")

ggplot(d, aes(x=pH, y=ExAl)) + geom_point() +
  stat_smooth(method="loess") +
  labs(x = "pH", y="Exchangeable Aluminum", title = "pH and Aluminum relationship")

ggplot(d, aes(x=Total.C, y=Total.N)) + geom_point() + 
  stat_smooth(method="loess") +
  labs(x = "Total Carbon", y="Total Nitrogen", title = "Carbon and Nitrogen relationship")

The soil characteristics are moving in the manner that is consistent with our understanding of soil chemical processes.

Save clean demographic and soil data to external file

write.csv(d, file="data/shs rw baseline.csv")
save(d, file="data/shs rw baseline.Rdata")

2.3.4 Map of baseline observations

Produce a simple map of where our observations are

See here for more on using markerClusterOptions in leaflet.

In the map below, the larger green circles are Tubura farmers and the smaller blue circles are control farmers.

e <- d[!is.na(d$lon),]
ss <- SpatialPointsDataFrame(coords = e[, c("lon", "lat")], data=e)
pal <- colorNumeric(c("navy", "green"), domain=unique(ss$client))
map <- leaflet() %>% addTiles() %>%
  setView(lng=rwanda$longitude, lat=rwanda$latitude, zoom=8) %>%
  addCircleMarkers(lng=ss$lon, lat=ss$lat, 
                   radius= ifelse(ss$client==1, 10,6),
                   color = pal(ss$client),
clusterOptions = markerClusterOptions(disableClusteringAtZoom=13, spiderfyOnMaxZoom=FALSE))
map

3 Summary statistics

3.0.1 Table of final baseline breakdown

count <- d %>% group_by(district) %>% 
  dplyr::summarize(
    t.count = sum(ifelse(client==1,1,0)),
    c.count = sum(ifelse(client==0,1,0)),
    total = n()
  ) %>% ungroup()
count <- as.data.frame(count)
write.csv(count, file="data/final rw sample breakdown.csv", row.names=F)
#as.data.frame(count)

3.1 Baseline balance

Let’s see how balanced our farmers are in terms of demographic variables. Tubura farmers were selected based on (list criteria) and control farmers in the same area tha fit the same criteria were also selected. No matching process has been performed to identify the control farmers that most closely resemble the Tubura farmers in the sample. These results are entirely reflecting the balance inherent in the identification process, not any statistical matching of treatment and control.

d$valley <- ifelse(d$general_field_infofield_location=="valley", 1,0)
d$hillside <- ifelse(d$general_field_infofield_location=="hillside", 1,0)
d$hilltop <- ifelse(d$general_field_infofield_location=="hilltop", 1,0)
names(d)[names(d)=="general_field_infograde_hill"] <- "slope"

cor(d[, grep("betail_", names(d))], use='complete.obs')

                       betail_ownedn_inka betail_ownedn_ihene betail_ownedn_inkoko betail_ownedn_ingurube betail_ownedn_intama
betail_ownedn_inka             1.00000000         0.035195326           0.09700986            0.021287801          0.040572776
betail_ownedn_ihene            0.03519533         1.000000000           0.12626563           -0.009531107         -0.002990226
betail_ownedn_inkoko           0.09700986         0.126265627           1.00000000            0.049825561          0.025680156
betail_ownedn_ingurube         0.02128780        -0.009531107           0.04982556            1.000000000          0.020300946
betail_ownedn_intama           0.04057278        -0.002990226           0.02568016            0.020300946          1.000000000

names(d)[grep("betail_", names(d))] <- c("cows", "goats", "chickens", "pigs", "sheep")
d$own.cows <- ifelse(d$cows>0 & !is.na(d$cows), 1,0)
d$own.goats <- ifelse(d$goats>0 & !is.na(d$goats), 1,0)
d$own.chickens <- ifelse(d$chickens>0 & !is.na(d$chickens), 1,0)
d$own.pigs <- ifelse(d$pigs>0 & !is.na(d$pigs), 1,0)
d$own.sheep <- ifelse(d$sheep>0 & !is.na(d$sheep), 1,0)

3.2 Crop selection in 15B

names(d)[names(d)=="inputuse_15b_priorculture_15b_1"] <-"crop_15b"
d$crop_15b <- tolower(d$crop_15b)
sort(prop.table(table(d$crop_15b, useNA = 'ifany')))

         kor         hwag        sheke       coffee          cer        uburo         teke       insina         shaz          nyo          gan 
0.0004100041 0.0008200082 0.0008200082 0.0012300123 0.0024600246 0.0036900369 0.0041000410 0.0057400574 0.0086100861 0.0131201312 0.0196801968 
   other_veg          ray          yum          gor       ntacyo         soya          jum          big         saka          shy 
0.0209102091 0.0524805248 0.0586305863 0.0594505945 0.0594505945 0.0791307913 0.0914309143 0.1599015990 0.1648216482 0.1931119311 

d$climbing_bean <- ifelse(d$crop_15b=="shy", 1,0)
d$sorghum <- ifelse(d$crop_15b=="saka", 1,0)
d$bush_bean <- ifelse(d$crop_15b=="big", 1,0)
d$maize <- ifelse(d$crop_15b=="gor", 1,0)

out.list <- c("female", "age", "hhsize", "own", "field.size",
              "n_season_fert", "n_season_compost", "n_season_lime", "n_season_fallow", "n_seasons_leg_1", "n_seasons_leg_2", "slope",  "alt", "valley", "hillside", "hilltop", "cows", "goats", "chickens", "pigs", "sheep", "climbing_bean", "sorghum", "bush_bean","maize", soilVars, "own.cows", "own.goats", "own.chickens", "own.pigs", "own.sheep", "black_soil", "red_soil", "sandy_soil")
output <- do.call(rbind, lapply(out.list, function(x) {
  
  out <- t.test(d[,x] ~ d[,"client"], data=d)
  tab <- data.frame(out[[5]][[2]],out[[5]][[1]], out[3])
  tab[,1:2] <- round(tab[,1:2],3)
  names(tab) <- c(names(out[[5]]), "pvalue")
  return(tab)
}))
# use p.adjust with bonferroni correction
output$pvalue <- p.adjust(output$pvalue, method="fdr")
rownames(output) <- out.list
output <- output[order(output$pvalue),]
output$pvalue <- ifelse(output[, 3] < 0.001, "< 0.001", round(output[, 3], 3)) 
colnames(output) <- c("Tubura Client","Non-Tubura", "p-value")  

print(kable(output))

	Tubura Client	Non-Tubura	p-value
n_season_fert	3.270	0.835	< 0.001
female	0.494	0.649	< 0.001
own.cows	0.681	0.542	< 0.001
age	44.020	48.070	< 0.001
hhsize	5.418	4.897	< 0.001
n_seasons_leg_2	2.533	3.086	< 0.001
own.chickens	0.340	0.267	< 0.001
own.pigs	0.355	0.281	< 0.001
n_season_lime	0.227	0.132	< 0.001
pigs	0.525	0.394	0.006
chickens	1.217	0.898	0.01
hillside	0.682	0.733	0.02
own	0.931	0.956	0.027
maize	0.072	0.047	0.027
valley	0.267	0.224	0.03
cows	1.174	0.919	0.03
sorghum	0.146	0.184	0.03
sheep	0.212	0.144	0.041
climbing_bean	0.212	0.174	0.041
own.sheep	0.105	0.078	0.046
n_season_fallow	0.699	0.566	0.083
n_season_compost	5.803	5.524	0.124
m3.Ca	798.870	843.860	0.143
pH	5.464	5.502	0.209
field.size	667.682	606.312	0.29
ExAl	0.604	0.578	0.404
n_seasons_leg_1	2.220	2.133	0.568
hilltop	0.051	0.044	0.568
bush_bean	0.154	0.166	0.568
m3.Mg	207.955	211.689	0.568
Total.C	2.072	2.087	0.568
slope	10.236	10.460	0.615
Total.N	0.152	0.153	0.615
sandy_soil	0.144	0.149	0.823
black_soil	0.537	0.543	0.835
alt	1570.722	1565.840	0.841
goats	1.074	1.057	0.841
red_soil	0.280	0.276	0.841
own.goats	0.483	0.486	0.892

Pre-PSM table 1 summary

table1vars <- c("pH", "Total.C", "Total.N", "m3.Ca", "m3.Mg")
table1 <- output[rownames(output) %in% table1vars, ]
write.csv(table1, file=paste("output","rw table1.csv", sep = "/"), row.names=T)

#write table
write.csv(output, file=paste("output", "baseline balance.csv", sep="/"), row.names=T)

# write baseline balance table for ES per his table layouts
t4order <- c("age", "female", "hhsize", "own")
table4vars <- paste(t4order, collapse="|")
rw.table4 <- output[grepl(table4vars, rownames(output)),]
rw.table4 <- rw.table4[order(match(rownames(rw.table4), t4order)),]
write.csv(rw.table4, file=paste("output", "pre match balance table4.csv", sep="/"),
          row.names = T)
t5order <- c("field.size", "slope", "alt", "hilltop", "hillside", "valley",
                      "climbing_bean", "sorghum", "bush_bean", "maize")
table5vars <- paste(t5order,collapse="|")
rw.table5 <- output[grepl(table5vars, rownames(output)),]
rw.table5 <- rw.table5[order(match(rownames(rw.table5), t5order)),]
write.csv(rw.table5, file=paste("output", "pre match balance table5.csv", sep = "/"),
          row.names = T)
t6order <- c("own.cows", "cows", "own.pigs", "pigs", "own.sheep", "sheep", "own.goats", "goats","own.chickens", "chickens")
table6vars <- paste(t6order, collapse = "|")
rw.table6 <- output[grep(table6vars, rownames(output)), ]
rw.table6 <- rw.table6[order(match(rownames(rw.table6), t6order)),]
write.csv(rw.table6, file=paste("output", "pre match balance table6.csv", sep = "/"), 
          row.names=T)

3.3 Overall balance interpretation

Demographic variables We are not well balanced along the main demographic variables we collected, sex, age and HH size. For the purposes of inference we can test some matching algorithms to improve the match between Tubura and control farmers.

Agricultural practice variables We are decently balanced along agricultural practice variables. Our course of action here is similiar to our options with the demographic variables.

Soil Variables We are balanced on the primary soil variables of interest betwen our Tubura farmers and the comparison farmers.

3.4 Baseline balance by district

dist.output <- do.call(rbind, lapply(split(d, d$district), function(x) {
  
  tab <- do.call(rbind, lapply(out.list, function(y) {
    
    out <- t.test(x[,y] ~ x[,"client"], data=x)
    tab <- data.frame(out[[5]][[2]],out[[5]][[1]], out[3])
    tab[,1:2] <- round(tab[,1:2],3)
    names(tab) <- c(names(out[[5]]), "pvalue")
    #tab[,3] <- p.adjust(tab[,3], method="holm")
    #tab[,3] <- ifelse(tab[,3] < 0.001, "< 0.001", round(tab[,3],3))
    #print(tab)
    return(tab)
  }))
  
  return(data.frame(district = unique(x$district), tab))
}))
rownames(dist.output) <- NULL
dist.output$variable <- rep(out.list,length(unique(d$district)))    
# order variables 
dist.output <- dist.output[, c(1, 5, 2:4)]
dist.output$pvalue <- p.adjust(dist.output$pvalue, method="fdr")
dist.output <- dist.output[order(dist.output$pvalue),]
dist.output$pvalue <- ifelse(dist.output$pvalue < 0.001, "< 0.001", round(dist.output$pvalue,3))
colnames(dist.output) <- c("District", "Varible", "Tubura Client","Non-Tubura", "p-value")  

print(kable(dist.output))

	District	Varible	Tubura Client	Non-Tubura	p-value
162	Karongi	n_season_fert	3.809	0.449	< 0.001
240	Mugonero	n_season_fert	4.313	0.592	< 0.001
435	Rutsiro	n_season_fert	4.200	1.072	< 0.001
318	Nyamasheke	n_season_fert	5.000	1.500	< 0.001
474	Rwamagana	n_season_fert	2.723	1.064	< 0.001
123	Huye	n_season_fert	2.268	0.322	< 0.001
461	Rutsiro	own.cows	0.824	0.542	< 0.001
45	Gatsibo_NLWH	n_season_fert	1.500	0.296	< 0.001
279	Nyamagabe	n_season_fert	4.000	1.582	< 0.001
132	Huye	hillside	0.696	0.922	< 0.001
131	Huye	valley	0.259	0.052	< 0.001
320	Nyamasheke	n_season_lime	0.322	0.041	< 0.001
201	Kayonza	n_season_fert	2.093	0.776	0.001
470	Rwamagana	age	44.009	51.882	0.002
157	Karongi	female	0.463	0.671	0.005
393	Nyaruguru	hhsize	6.217	4.717	0.008
110	Gisagara	own.cows	0.696	0.326	0.008
315	Nyamasheke	hhsize	5.698	4.719	0.008
112	Gisagara	own.chickens	0.370	0.065	0.009
113	Gisagara	own.pigs	0.565	0.217	0.012
95	Gisagara	cows	1.043	0.413	0.018
119	Huye	age	43.491	49.400	0.021
274	Nyamagabe	female	0.482	0.764	0.038
337	Nyamasheke	maize	0.081	0.007	0.038
352	Nyanza	female	0.234	0.543	0.038
357	Nyanza	n_season_fert	1.298	0.152	0.038
97	Gisagara	chickens	1.196	0.174	0.039
2	Gatsibo_LWH	age	41.931	50.138	0.042
41	Gatsibo_NLWH	age	40.023	46.210	0.042
40	Gatsibo_NLWH	female	0.326	0.556	0.044
502	Rwamagana	own.chickens	0.411	0.227	0.052
242	Mugonero	n_season_lime	0.176	0.023	0.06
43	Gatsibo_NLWH	own	0.907	1.000	0.061
469	Rwamagana	female	0.554	0.736	0.063
196	Kayonza	female	0.407	0.672	0.067
149	Huye	own.cows	0.670	0.487	0.071
237	Mugonero	hhsize	5.802	5.031	0.074
432	Rutsiro	hhsize	5.624	4.867	0.08
1	Gatsibo_LWH	female	0.241	0.483	0.084
181	Karongi	maize	0.086	0.019	0.084
236	Mugonero	age	44.290	49.331	0.091
305	Nyamagabe	own.cows	0.732	0.491	0.105
319	Nyamasheke	n_season_compost	6.517	5.418	0.106
365	Nyanza	valley	0.298	0.087	0.107
366	Nyanza	hillside	0.702	0.913	0.107
334	Nyamasheke	climbing_bean	0.416	0.274	0.109
310	Nyamagabe	black_soil	0.446	0.218	0.11
62	Gatsibo_NLWH	sorghum	0.279	0.469	0.116
391	Nyaruguru	female	0.391	0.652	0.121
437	Rutsiro	n_season_lime	0.327	0.096	0.138
134	Huye	cows	1.161	0.774	0.144
224	Kayonza	Total.N	0.179	0.194	0.15
256	Mugonero	climbing_bean	0.321	0.192	0.159
363	Nyanza	slope	7.362	9.239	0.159
491	Rwamagana	sorghum	0.188	0.327	0.159
396	Nyaruguru	n_season_fert	2.130	1.065	0.174
81	Gisagara	hhsize	5.348	4.326	0.179
98	Gisagara	pigs	0.609	0.304	0.179
137	Huye	pigs	0.964	0.748	0.179
152	Huye	own.pigs	0.759	0.617	0.179
324	Nyamasheke	slope	14.315	17.185	0.179
426	Nyaruguru	own.sheep	0.109	0.000	0.19
276	Nyamagabe	hhsize	5.482	4.673	0.206
6	Gatsibo_LWH	n_season_fert	2.552	1.500	0.217
450	Rutsiro	sheep	0.467	0.247	0.217
120	Huye	hhsize	5.134	4.574	0.223
140	Huye	sorghum	0.241	0.374	0.223
500	Rwamagana	own.cows	0.545	0.400	0.227
245	Mugonero	n_seasons_leg_2	3.131	4.077	0.231
128	Huye	n_seasons_leg_2	3.732	4.860	0.243
50	Gatsibo_NLWH	n_seasons_leg_2	2.977	3.889	0.244
292	Nyamagabe	chickens	0.857	0.309	0.244
463	Rutsiro	own.chickens	0.291	0.193	0.254
87	Gisagara	n_season_fallow	0.500	0.087	0.255
89	Gisagara	n_seasons_leg_2	2.370	3.283	0.255
191	Karongi	own.pigs	0.401	0.291	0.255
307	Nyamagabe	own.chickens	0.286	0.127	0.255
457	Rutsiro	pH	5.275	5.396	0.255
59	Gatsibo_NLWH	pigs	0.244	0.062	0.262
207	Kayonza	slope	3.296	1.690	0.275
322	Nyamasheke	n_seasons_leg_1	2.309	1.733	0.275
355	Nyanza	own	0.915	1.000	0.275
441	Rutsiro	slope	15.521	14.030	0.276
362	Nyanza	n_seasons_leg_2	2.085	3.478	0.277
472	Rwamagana	own	0.911	0.973	0.285
445	Rutsiro	hilltop	0.097	0.042	0.293
84	Gisagara	n_season_fert	1.087	0.348	0.3
420	Nyaruguru	Total.C	2.056	2.176	0.3
164	Karongi	n_season_lime	0.099	0.013	0.31
275	Nyamagabe	age	43.768	49.018	0.31
485	Rwamagana	cows	1.062	0.518	0.31
108	Gisagara	Total.C	1.836	1.967	0.316
225	Kayonza	Total.C	2.355	2.520	0.316
411	Nyaruguru	sheep	0.196	0.000	0.316
96	Gisagara	goats	1.522	0.935	0.319
477	Rwamagana	n_season_fallow	1.214	0.718	0.319
382	Nyanza	ExAl	0.168	0.262	0.324
503	Rwamagana	own.pigs	0.277	0.173	0.324
487	Rwamagana	chickens	1.580	0.964	0.329
370	Nyanza	chickens	2.298	1.174	0.331
172	Karongi	hilltop	0.068	0.025	0.341
303	Nyamagabe	Total.C	2.282	2.160	0.341
347	Nyamasheke	own.pigs	0.409	0.308	0.341
407	Nyaruguru	cows	1.196	0.891	0.343
92	Gisagara	valley	0.283	0.130	0.346
203	Kayonza	n_season_lime	0.537	0.707	0.35
455	Rutsiro	m3.Ca	568.202	643.814	0.35
294	Nyamagabe	sheep	0.286	0.091	0.352
46	Gatsibo_NLWH	n_season_compost	2.547	3.420	0.353
226	Kayonza	ExAl	0.125	0.162	0.364
233	Kayonza	red_soil	0.204	0.086	0.364
348	Nyamasheke	own.sheep	0.060	0.021	0.367
142	Huye	maize	0.062	0.017	0.374
197	Kayonza	age	42.093	46.500	0.379
55	Gatsibo_NLWH	hilltop	0.012	0.062	0.384
216	Kayonza	sheep	0.037	0.172	0.384
221	Kayonza	m3.Ca	1685.356	1998.563	0.384
422	Nyaruguru	own.cows	0.826	0.674	0.397
79	Gisagara	female	0.435	0.609	0.403
425	Nyaruguru	own.pigs	0.609	0.435	0.403
173	Karongi	cows	1.383	1.127	0.404
312	Nyamagabe	sandy_soil	0.089	0.200	0.407
410	Nyaruguru	pigs	0.783	0.457	0.407
262	Mugonero	pH	5.075	5.177	0.43
375	Nyanza	bush_bean	0.255	0.413	0.436
266	Mugonero	own.cows	0.786	0.700	0.441
105	Gisagara	m3.Mg	218.396	247.896	0.446
223	Kayonza	pH	6.111	6.242	0.446
260	Mugonero	m3.Ca	458.124	521.824	0.446
390	Nyanza	sandy_soil	0.298	0.457	0.449
158	Karongi	age	44.741	47.285	0.453
180	Karongi	bush_bean	0.074	0.127	0.453
353	Nyanza	age	45.128	50.152	0.453
333	Nyamasheke	sheep	0.161	0.027	0.464
379	Nyanza	pH	6.049	5.875	0.464
323	Nyamasheke	n_seasons_leg_2	2.101	2.660	0.469
424	Nyaruguru	own.chickens	0.283	0.152	0.478
446	Rutsiro	cows	1.442	1.048	0.478
460	Rutsiro	ExAl	0.807	0.706	0.493
33	Gatsibo_LWH	own.goats	0.552	0.414	0.493
185	Karongi	Total.N	0.138	0.133	0.493
74	Gatsibo_NLWH	own.pigs	0.128	0.062	0.496
104	Gisagara	m3.Ca	920.553	1100.991	0.496
153	Huye	own.sheep	0.054	0.017	0.496
436	Rutsiro	n_season_compost	8.176	7.639	0.496
293	Nyamagabe	pigs	1.000	0.800	0.499
438	Rutsiro	n_season_fallow	0.473	0.289	0.499
265	Mugonero	ExAl	0.928	0.814	0.516
103	Gisagara	maize	0.043	0.000	0.516
250	Mugonero	hilltop	0.015	0.000	0.516
297	Nyamagabe	bush_bean	0.000	0.036	0.516
377	Nyanza	m3.Ca	1119.490	966.731	0.516
398	Nyaruguru	n_season_lime	0.043	0.000	0.516
507	Rwamagana	sandy_soil	0.018	0.000	0.516
174	Karongi	goats	0.883	1.070	0.518
52	Gatsibo_NLWH	alt	1012.158	1129.172	0.521
102	Gisagara	bush_bean	0.217	0.348	0.521
118	Huye	female	0.500	0.591	0.521
199	Kayonza	own	0.963	0.897	0.521
231	Kayonza	own.sheep	0.037	0.103	0.521
349	Nyamasheke	black_soil	0.537	0.616	0.521
430	Rutsiro	female	0.545	0.620	0.521
109	Gisagara	ExAl	0.349	0.256	0.525
371	Nyanza	pigs	0.085	0.217	0.525
386	Nyanza	own.pigs	0.064	0.152	0.525
419	Nyaruguru	Total.N	0.153	0.161	0.525
117	Gisagara	sandy_soil	0.239	0.370	0.53
309	Nyamagabe	own.sheep	0.179	0.091	0.531
11	Gatsibo_LWH	n_seasons_leg_2	1.983	2.737	0.531
32	Gatsibo_LWH	own.cows	0.500	0.379	0.531
58	Gatsibo_NLWH	chickens	1.674	1.000	0.531
66	Gatsibo_NLWH	m3.Mg	237.170	258.718	0.531
93	Gisagara	hillside	0.587	0.717	0.531
209	Kayonza	valley	0.556	0.431	0.531
214	Kayonza	chickens	1.000	0.466	0.531
220	Kayonza	maize	0.019	0.069	0.531
283	Nyamagabe	n_seasons_leg_1	1.625	1.145	0.531
383	Nyanza	own.cows	0.660	0.522	0.531
394	Nyaruguru	own	0.848	0.935	0.531
465	Rutsiro	own.sheep	0.212	0.157	0.531
479	Rwamagana	n_seasons_leg_2	1.324	1.700	0.531
488	Rwamagana	pigs	0.509	0.318	0.531
17	Gatsibo_LWH	cows	0.759	0.552	0.554
163	Karongi	n_season_compost	7.068	6.557	0.554
159	Karongi	hhsize	5.185	4.905	0.559
314	Nyamasheke	age	45.738	47.938	0.559
111	Gisagara	own.goats	0.630	0.500	0.56
136	Huye	chickens	1.795	1.348	0.56
160	Karongi	own	0.957	0.981	0.56
211	Kayonza	hilltop	0.093	0.172	0.563
339	Nyamasheke	m3.Mg	167.529	152.133	0.567
311	Nyamagabe	red_soil	0.464	0.582	0.567
127	Huye	n_seasons_leg_1	1.393	1.043	0.571
143	Huye	m3.Ca	812.314	882.646	0.574
447	Rutsiro	goats	0.515	0.663	0.582
480	Rwamagana	slope	3.705	4.273	0.582
107	Gisagara	Total.N	0.142	0.149	0.586
76	Gatsibo_NLWH	black_soil	0.616	0.704	0.592
106	Gisagara	pH	5.706	5.842	0.6
171	Karongi	hillside	0.784	0.835	0.602
18	Gatsibo_LWH	goats	1.345	1.000	0.608
19	Gatsibo_LWH	chickens	1.052	0.690	0.608
146	Huye	Total.N	0.134	0.137	0.608
219	Kayonza	bush_bean	0.222	0.138	0.608
298	Nyamagabe	maize	0.089	0.036	0.608
389	Nyanza	red_soil	0.106	0.043	0.608
442	Rutsiro	alt	1945.122	1896.140	0.608
462	Rutsiro	own.goats	0.273	0.331	0.608
71	Gatsibo_NLWH	own.cows	0.570	0.481	0.61
374	Nyanza	sorghum	0.298	0.196	0.61
268	Mugonero	own.chickens	0.382	0.315	0.62
154	Huye	black_soil	0.509	0.583	0.627
64	Gatsibo_NLWH	maize	0.093	0.049	0.637
213	Kayonza	goats	1.759	1.397	0.637
344	Nyamasheke	own.cows	0.638	0.575	0.638
47	Gatsibo_NLWH	n_season_lime	0.058	0.025	0.641
228	Kayonza	own.goats	0.685	0.586	0.641
234	Kayonza	sandy_soil	0.037	0.086	0.641
57	Gatsibo_NLWH	goats	1.198	1.654	0.66
3	Gatsibo_LWH	hhsize	5.362	5.741	0.66
4	Gatsibo_LWH	own	0.983	1.000	0.66
56	Gatsibo_NLWH	cows	0.895	0.741	0.66
78	Gatsibo_NLWH	sandy_soil	0.279	0.210	0.66
88	Gisagara	n_seasons_leg_1	2.304	2.870	0.66
115	Gisagara	black_soil	0.587	0.478	0.66
121	Huye	own	0.973	0.991	0.66
125	Huye	n_season_lime	0.036	0.113	0.66
184	Karongi	pH	5.423	5.481	0.66
186	Karongi	Total.C	1.898	1.851	0.66
200	Kayonza	field.size	572.833	423.776	0.66
217	Kayonza	climbing_bean	0.000	0.017	0.66
235	Mugonero	female	0.656	0.715	0.66
238	Mugonero	own	0.947	0.915	0.66
259	Mugonero	maize	0.000	0.008	0.66
350	Nyamasheke	red_soil	0.315	0.260	0.66
368	Nyanza	cows	1.170	0.891	0.66
376	Nyanza	maize	0.128	0.065	0.66
395	Nyaruguru	field.size	570.022	495.239	0.66
414	Nyaruguru	bush_bean	0.065	0.022	0.66
415	Nyaruguru	maize	0.022	0.000	0.66
434	Rutsiro	field.size	584.327	429.614	0.66
443	Rutsiro	valley	0.152	0.193	0.66
449	Rutsiro	pigs	0.315	0.241	0.66
489	Rwamagana	sheep	0.500	0.364	0.66
493	Rwamagana	maize	0.027	0.009	0.66
251	Mugonero	cows	1.328	1.169	0.664
456	Rutsiro	m3.Mg	163.400	173.586	0.664
178	Karongi	climbing_bean	0.407	0.354	0.664
218	Kayonza	sorghum	0.167	0.103	0.669
8	Gatsibo_LWH	n_season_lime	0.500	0.328	0.678
37	Gatsibo_LWH	black_soil	0.172	0.241	0.678
38	Gatsibo_LWH	red_soil	0.638	0.552	0.678
53	Gatsibo_NLWH	valley	0.756	0.691	0.678
83	Gisagara	field.size	493.957	559.261	0.678
135	Huye	goats	0.955	0.809	0.678
145	Huye	pH	5.557	5.616	0.678
167	Karongi	n_seasons_leg_2	3.174	3.558	0.678
182	Karongi	m3.Ca	735.109	793.491	0.678
183	Karongi	m3.Mg	238.490	256.902	0.678
239	Mugonero	field.size	441.359	391.208	0.678
290	Nyamagabe	cows	0.911	0.727	0.678
325	Nyamasheke	alt	1676.658	1702.940	0.678
351	Nyamasheke	sandy_soil	0.114	0.082	0.678
385	Nyanza	own.chickens	0.532	0.435	0.678
409	Nyaruguru	chickens	0.652	0.391	0.678
421	Nyaruguru	ExAl	0.851	0.956	0.678
454	Rutsiro	maize	0.176	0.217	0.678
5	Gatsibo_LWH	field.size	776.345	919.724	0.688
492	Rwamagana	bush_bean	0.366	0.309	0.688
48	Gatsibo_NLWH	n_season_fallow	0.744	0.543	0.694
91	Gisagara	alt	1036.055	923.525	0.694
187	Karongi	ExAl	0.631	0.573	0.694
308	Nyamagabe	own.pigs	0.732	0.655	0.694
313	Nyamasheke	female	0.644	0.692	0.7
332	Nyamasheke	pigs	0.779	0.582	0.7
418	Nyaruguru	pH	5.049	4.957	0.7
150	Huye	own.goats	0.518	0.461	0.706
141	Huye	bush_bean	0.196	0.243	0.706
423	Nyaruguru	own.goats	0.348	0.435	0.711
31	Gatsibo_LWH	ExAl	0.174	0.203	0.714
405	Nyaruguru	hillside	0.957	0.913	0.714
406	Nyaruguru	hilltop	0.043	0.087	0.714
101	Gisagara	sorghum	0.435	0.522	0.72
254	Mugonero	pigs	0.412	0.331	0.722
340	Nyamasheke	pH	5.136	5.089	0.727
155	Huye	red_soil	0.179	0.139	0.729
34	Gatsibo_LWH	own.chickens	0.345	0.276	0.738
73	Gatsibo_NLWH	own.chickens	0.302	0.247	0.738
189	Karongi	own.goats	0.494	0.538	0.743
270	Mugonero	own.sheep	0.122	0.092	0.752
403	Nyaruguru	alt	1791.131	1814.738	0.752
63	Gatsibo_NLWH	bush_bean	0.174	0.222	0.754
364	Nyanza	alt	1508.930	1529.554	0.754
504	Rwamagana	own.sheep	0.223	0.182	0.754
10	Gatsibo_LWH	n_seasons_leg_1	4.345	4.690	0.756
80	Gisagara	age	44.761	46.848	0.756
126	Huye	n_season_fallow	0.661	0.487	0.756
133	Huye	hilltop	0.045	0.026	0.756
138	Huye	sheep	0.062	0.035	0.756
194	Karongi	red_soil	0.327	0.367	0.756
215	Kayonza	pigs	0.315	0.224	0.756
269	Mugonero	own.pigs	0.244	0.285	0.756
272	Mugonero	red_soil	0.252	0.292	0.756
278	Nyamagabe	field.size	310.929	283.782	0.756
291	Nyamagabe	goats	0.750	0.618	0.756
429	Nyaruguru	sandy_soil	0.109	0.065	0.756
475	Rwamagana	n_season_compost	4.009	3.691	0.756
72	Gatsibo_NLWH	own.goats	0.512	0.568	0.757
124	Huye	n_season_compost	5.232	4.878	0.757
176	Karongi	pigs	0.531	0.449	0.757
161	Karongi	field.size	492.262	448.278	0.76
384	Nyanza	own.goats	0.617	0.543	0.76
388	Nyanza	black_soil	0.574	0.500	0.76
354	Nyanza	hhsize	5.213	4.891	0.763
360	Nyanza	n_season_fallow	1.149	0.870	0.769
499	Rwamagana	ExAl	0.348	0.320	0.779
20	Gatsibo_LWH	pigs	0.138	0.207	0.787
60	Gatsibo_NLWH	sheep	0.047	0.025	0.787
227	Kayonza	own.cows	0.370	0.310	0.787
229	Kayonza	own.chickens	0.185	0.138	0.787
230	Kayonza	own.pigs	0.222	0.172	0.787
317	Nyamasheke	field.size	791.856	620.884	0.787
427	Nyaruguru	black_soil	0.630	0.696	0.787
467	Rutsiro	red_soil	0.315	0.349	0.787
473	Rwamagana	field.size	918.893	837.927	0.787
26	Gatsibo_LWH	m3.Ca	1281.010	1186.023	0.788
42	Gatsibo_NLWH	hhsize	5.349	5.160	0.788
147	Huye	Total.C	1.869	1.895	0.788
244	Mugonero	n_seasons_leg_1	1.336	1.177	0.788
282	Nyamagabe	n_season_fallow	0.268	0.182	0.788
378	Nyanza	m3.Mg	242.303	230.433	0.788
433	Rutsiro	own	0.915	0.934	0.788
85	Gisagara	n_season_compost	4.326	3.957	0.791
70	Gatsibo_NLWH	ExAl	0.202	0.220	0.793
44	Gatsibo_NLWH	field.size	1319.302	1459.901	0.796
468	Rutsiro	sandy_soil	0.176	0.151	0.796
408	Nyaruguru	goats	0.609	0.739	0.805
486	Rwamagana	goats	1.768	1.582	0.805
23	Gatsibo_LWH	sorghum	0.276	0.328	0.805
372	Nyanza	sheep	0.021	0.043	0.805
387	Nyanza	own.sheep	0.021	0.043	0.805
496	Rwamagana	pH	5.697	5.735	0.805
27	Gatsibo_LWH	m3.Mg	253.408	241.489	0.812
36	Gatsibo_LWH	own.sheep	0.034	0.017	0.812
399	Nyaruguru	n_season_fallow	0.370	0.261	0.812
24	Gatsibo_LWH	bush_bean	0.397	0.345	0.815
82	Gisagara	own	0.826	0.870	0.815
277	Nyamagabe	own	0.964	0.982	0.819
373	Nyanza	climbing_bean	0.043	0.022	0.819
271	Mugonero	black_soil	0.527	0.492	0.821
431	Rutsiro	age	43.685	44.614	0.821
498	Rwamagana	Total.C	2.161	2.188	0.821
501	Rwamagana	own.goats	0.536	0.573	0.821
90	Gisagara	slope	8.391	7.739	0.823
9	Gatsibo_LWH	n_season_fallow	0.397	0.293	0.834
190	Karongi	own.chickens	0.407	0.437	0.835
338	Nyamasheke	m3.Ca	504.693	482.970	0.836
28	Gatsibo_LWH	pH	6.055	6.005	0.837
188	Karongi	own.cows	0.765	0.741	0.839
206	Kayonza	n_seasons_leg_2	1.111	1.259	0.839
122	Huye	field.size	599.607	567.922	0.845
205	Kayonza	n_seasons_leg_1	2.019	2.155	0.845
281	Nyamagabe	n_season_lime	0.500	0.400	0.845
412	Nyaruguru	climbing_bean	0.196	0.239	0.845
39	Gatsibo_LWH	sandy_soil	0.172	0.207	0.851
77	Gatsibo_NLWH	red_soil	0.035	0.049	0.851
192	Karongi	own.sheep	0.080	0.095	0.851
210	Kayonza	hillside	0.352	0.397	0.851
253	Mugonero	chickens	1.076	0.954	0.851
286	Nyamagabe	alt	2124.389	2110.970	0.851
288	Nyamagabe	hillside	0.821	0.855	0.851
296	Nyamagabe	sorghum	0.036	0.055	0.851
301	Nyamagabe	pH	4.838	4.869	0.851
306	Nyamagabe	own.goats	0.464	0.418	0.851
397	Nyaruguru	n_season_compost	5.696	6.043	0.851
481	Rwamagana	alt	990.930	956.133	0.851
505	Rwamagana	black_soil	0.652	0.682	0.851
246	Mugonero	slope	15.939	16.462	0.853
267	Mugonero	own.goats	0.542	0.569	0.862
400	Nyaruguru	n_seasons_leg_1	2.043	2.348	0.862
482	Rwamagana	valley	0.402	0.373	0.862
151	Huye	own.chickens	0.420	0.391	0.865
289	Nyamagabe	hilltop	0.054	0.036	0.865
343	Nyamasheke	ExAl	1.006	1.037	0.868
13	Gatsibo_LWH	alt	1094.532	1135.001	0.872
316	Nyamasheke	own	0.926	0.938	0.873
483	Rwamagana	hillside	0.536	0.564	0.873
476	Rwamagana	n_season_lime	0.348	0.318	0.887
75	Gatsibo_NLWH	own.sheep	0.035	0.025	0.894
284	Nyamagabe	n_seasons_leg_2	3.161	2.982	0.894
356	Nyanza	field.size	838.926	766.217	0.894
361	Nyanza	n_seasons_leg_1	2.511	2.283	0.894
466	Rutsiro	black_soil	0.461	0.440	0.894
144	Huye	m3.Mg	215.213	211.384	0.894
336	Nyamasheke	bush_bean	0.087	0.075	0.894
129	Huye	slope	6.616	6.817	0.903
490	Rwamagana	climbing_bean	0.036	0.027	0.903
177	Karongi	sheep	0.167	0.196	0.905
195	Karongi	sandy_soil	0.025	0.019	0.908
264	Mugonero	Total.C	2.147	2.171	0.912
280	Nyamagabe	n_season_compost	7.857	7.655	0.912
300	Nyamagabe	m3.Mg	115.173	112.262	0.913
212	Kayonza	cows	0.685	0.828	0.913
249	Mugonero	hillside	0.855	0.869	0.913
67	Gatsibo_NLWH	pH	6.011	5.989	0.917
222	Kayonza	m3.Mg	347.261	354.791	0.917
304	Nyamagabe	ExAl	1.150	1.119	0.918
464	Rutsiro	own.pigs	0.194	0.181	0.926
94	Gisagara	hilltop	0.130	0.152	0.93
116	Gisagara	red_soil	0.152	0.130	0.93
416	Nyaruguru	m3.Ca	415.882	434.099	0.93
35	Gatsibo_LWH	own.pigs	0.121	0.103	0.932
444	Rutsiro	hillside	0.752	0.765	0.934
7	Gatsibo_LWH	n_season_compost	5.672	5.879	0.936
401	Nyaruguru	n_seasons_leg_2	3.178	3.370	0.937
471	Rwamagana	hhsize	4.920	5.000	0.938
241	Mugonero	n_season_compost	6.229	6.100	0.938
345	Nyamasheke	own.goats	0.430	0.445	0.938
287	Nyamagabe	valley	0.125	0.109	0.946
478	Rwamagana	n_seasons_leg_1	1.652	1.582	0.946
175	Karongi	chickens	1.463	1.386	0.949
232	Kayonza	black_soil	0.685	0.707	0.949
257	Mugonero	sorghum	0.061	0.054	0.949
428	Nyaruguru	red_soil	0.261	0.239	0.954
29	Gatsibo_LWH	Total.N	0.149	0.148	0.956
54	Gatsibo_NLWH	hillside	0.233	0.247	0.956
130	Huye	alt	1649.310	1655.403	0.956
148	Huye	ExAl	0.290	0.298	0.956
170	Karongi	valley	0.148	0.139	0.956
295	Nyamagabe	climbing_bean	0.161	0.145	0.956
342	Nyamasheke	Total.C	2.242	2.256	0.956
381	Nyanza	Total.C	1.763	1.747	0.956
198	Kayonza	hhsize	5.074	5.155	0.956
139	Huye	climbing_bean	0.089	0.096	0.96
156	Huye	sandy_soil	0.268	0.278	0.96
193	Karongi	black_soil	0.580	0.570	0.96
243	Mugonero	n_season_fallow	0.863	0.908	0.96
247	Mugonero	alt	1796.175	1787.253	0.96
261	Mugonero	m3.Mg	162.889	165.293	0.96
299	Nyamagabe	m3.Ca	329.187	335.265	0.96
330	Nyamasheke	goats	0.899	0.870	0.96
331	Nyamasheke	chickens	0.591	0.562	0.96
341	Nyamasheke	Total.N	0.162	0.161	0.96
358	Nyanza	n_season_compost	3.191	3.326	0.96
369	Nyanza	goats	1.234	1.283	0.96
417	Nyaruguru	m3.Mg	146.273	144.478	0.96
440	Rutsiro	n_seasons_leg_2	2.036	2.091	0.96
448	Rutsiro	chickens	0.879	0.964	0.96
458	Rutsiro	Total.N	0.149	0.150	0.96
494	Rwamagana	m3.Ca	1230.875	1244.512	0.96
506	Rwamagana	red_soil	0.330	0.318	0.96
21	Gatsibo_LWH	sheep	0.103	0.086	0.971
252	Mugonero	goats	1.321	1.292	0.973
439	Rutsiro	n_seasons_leg_1	3.388	3.440	0.974
30	Gatsibo_LWH	Total.C	1.915	1.906	0.999
65	Gatsibo_NLWH	m3.Ca	1198.728	1210.672	0.999
166	Karongi	n_seasons_leg_1	2.438	2.468	0.999
179	Karongi	sorghum	0.148	0.152	0.999
202	Kayonza	n_season_compost	3.481	3.534	0.999
204	Kayonza	n_season_fallow	0.463	0.483	0.999
255	Mugonero	sheep	0.221	0.231	0.999
321	Nyamasheke	n_season_fallow	0.651	0.671	0.999
326	Nyamasheke	valley	0.154	0.151	0.999
346	Nyamasheke	own.chickens	0.215	0.219	0.999
495	Rwamagana	m3.Mg	268.544	267.492	0.999
12	Gatsibo_LWH	slope	4.259	4.224	1
14	Gatsibo_LWH	valley	0.603	0.603	1
15	Gatsibo_LWH	hillside	0.397	0.397	1
49	Gatsibo_NLWH	n_seasons_leg_1	1.547	1.556	1
51	Gatsibo_NLWH	slope	4.058	4.099	1
68	Gatsibo_NLWH	Total.N	0.159	0.159	1
69	Gatsibo_NLWH	Total.C	2.009	2.012	1
86	Gisagara	n_season_lime	0.022	0.022	1
99	Gisagara	sheep	0.022	0.022	1
100	Gisagara	climbing_bean	0.022	0.022	1
114	Gisagara	own.sheep	0.022	0.022	1
165	Karongi	n_season_fallow	0.840	0.848	1
168	Karongi	slope	12.006	11.975	1
169	Karongi	alt	1807.818	1806.996	1
208	Kayonza	alt	1203.816	1202.646	1
248	Mugonero	valley	0.130	0.131	1
258	Mugonero	bush_bean	0.176	0.177	1
263	Mugonero	Total.N	0.145	0.145	1
273	Mugonero	sandy_soil	0.176	0.177	1
285	Nyamagabe	slope	12.571	12.618	1
302	Nyamagabe	Total.N	0.153	0.153	1
327	Nyamasheke	hillside	0.819	0.822	1
328	Nyamasheke	hilltop	0.027	0.027	1
329	Nyamasheke	cows	1.242	1.267	1
380	Nyanza	Total.N	0.126	0.126	1
392	Nyaruguru	age	48.457	48.304	1
402	Nyaruguru	slope	9.478	9.457	1
413	Nyaruguru	sorghum	0.304	0.304	1
451	Rutsiro	climbing_bean	0.327	0.331	1
453	Rutsiro	bush_bean	0.012	0.012	1
459	Rutsiro	Total.C	2.187	2.187	1
484	Rwamagana	hilltop	0.062	0.064	1
497	Rwamagana	Total.N	0.182	0.182	1
16	Gatsibo_LWH	hilltop	0.000	0.000	NA
22	Gatsibo_LWH	climbing_bean	0.000	0.000	NA
25	Gatsibo_LWH	maize	0.000	0.000	NA
61	Gatsibo_NLWH	climbing_bean	0.000	0.000	NA
335	Nyamasheke	sorghum	0.000	0.000	NA
359	Nyanza	n_season_lime	0.000	0.000	NA
367	Nyanza	hilltop	0.000	0.000	NA
404	Nyaruguru	valley	0.000	0.000	NA
452	Rutsiro	sorghum	0.000	0.000	NA

3.5 District balance interpretation

Demographic variables interpretation here.

Agricultural practice variables interpretation here

Soil Variables interpretation here

write.csv(dist.output, file=paste("output", "district balance.csv", sep="/"), row.names=T)

3.6 Baseline balance by Tubura tenure

Look at farmers by duration of tenure farming with Tubura. We want to understand, at least with an initial naive baseline sense, what is the cumulative effect of Tubura practices on soil health outcomes?

We will look only at current Tubura farmers and compare first year farmers to farmers with more experience with Tubura.

oafOnly <- d[which(d$client==1 & d$total.seasons>=1),]
nTenure <- oafOnly %>% group_by(total.seasons) %>% 
  dplyr::summarize(
    n = n()
  ) %>% ungroup() %>% as.data.frame()
nTenure$val <- paste(nTenure$total.seasons, " (", "n = ", nTenure$n, ")", sep = "")
for(i in 1:length(soilVars)){
print(
  ggplot(oafOnly, aes(x=as.factor(total.seasons), y=oafOnly[,soilVars[i]])) + 
    geom_boxplot() +
    scale_x_discrete(labels=nTenure$val) + 
    theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(x="Tubura Tenure", y=soilVars[i], title = paste("RW baseline soil by tenure - ", soilVars[i], sep = ""))
  )  
}

3.7 Tenure summaries

tenureSum <- aggregate(oafOnly[,out.list], by=list(oafOnly$total.seasons), function(x){
  round(mean(x, na.rm=T),2)
})
tenureSum <- as.data.frame(t(tenureSum))
colnames(tenureSum) <- c(paste(seq(1,14,1), " seas.", sep = ""))

print(kable(tenureSum))

	1 seas.	2 seas.	3 seas.	4 seas.	5 seas.	6 seas.	7 seas.	8 seas.	9 seas.	10 seas.	11 seas.	12 seas.	13 seas.	14 seas.
Group.1	1.00	2.00	3.00	4.00	5.00	6.00	7.00	8.00	9.00	10.00	11.00	12.00	13.00	14.00
female	0.43	0.48	0.60	0.49	0.58	0.59	0.54	0.50	0.65	0.60	0.75	0.54	0.55	0.48
age	42.52	42.77	44.33	45.68	43.07	43.96	43.71	50.91	51.85	42.67	48.25	49.00	52.73	49.52
hhsize	5.23	5.19	5.08	5.22	6.07	5.58	6.11	5.66	6.65	6.13	6.58	5.51	4.55	6.12
own	0.93	0.95	0.92	0.95	0.98	0.97	1.00	0.93	0.85	0.97	0.92	0.90	0.91	0.95
field.size	570.34	634.79	630.65	554.01	486.51	484.79	412.07	592.95	330.00	644.30	617.83	444.85	280.82	679.71
n_season_fert	1.78	2.33	2.63	3.29	4.67	4.90	5.93	5.59	6.00	5.63	7.25	7.08	7.18	6.79
n_season_compost	5.36	5.33	4.81	6.36	6.84	7.13	8.36	7.43	7.30	7.33	8.67	8.15	8.27	7.81
n_season_lime	0.17	0.27	0.36	0.31	0.22	0.26	0.07	0.07	0.20	0.27	0.08	0.15	0.09	0.48
n_season_fallow	0.64	0.50	0.77	0.91	0.67	0.66	0.64	0.77	1.10	0.47	0.42	0.23	1.82	0.67
n_seasons_leg_1	1.99	2.04	2.10	2.44	2.93	2.89	1.93	2.91	1.60	1.77	2.92	3.54	2.09	3.02
n_seasons_leg_2	2.47	2.48	2.03	2.34	2.71	2.62	4.75	3.50	2.75	2.50	3.92	1.45	2.82	2.12
slope	9.07	8.71	9.65	8.80	9.58	11.84	14.29	11.77	13.60	12.07	14.17	12.00	14.91	14.38
alt	1536.57	1491.09	1550.41	1516.91	1650.84	1582.27	1758.60	1639.74	1812.98	1755.70	1788.95	1644.45	1702.35	1633.76
valley	0.32	0.32	0.27	0.26	0.31	0.22	0.25	0.23	0.00	0.10	0.33	0.18	0.09	0.21
hillside	0.62	0.62	0.71	0.71	0.67	0.74	0.71	0.66	0.95	0.87	0.67	0.79	0.82	0.76
hilltop	0.06	0.06	0.03	0.03	0.02	0.05	0.04	0.11	0.05	0.03	0.00	0.03	0.09	0.02
cows	0.97	1.00	0.94	1.49	1.93	1.28	1.29	1.09	3.25	1.27	1.75	1.00	1.00	1.07
goats	0.99	1.11	1.00	1.27	1.40	1.00	0.75	1.16	1.20	0.87	0.83	1.08	0.45	1.26
chickens	1.08	1.19	1.08	1.21	1.31	0.87	1.71	1.61	1.20	1.40	3.58	1.72	0.45	1.00
pigs	0.54	0.52	0.58	0.52	0.38	0.47	0.46	0.55	0.45	0.60	0.25	0.82	0.64	0.83
sheep	0.20	0.21	0.29	0.35	0.18	0.16	0.86	0.23	0.15	0.40	0.00	0.05	0.18	0.17
climbing_bean	0.14	0.18	0.21	0.17	0.20	0.32	0.50	0.36	0.35	0.43	0.33	0.38	0.55	0.38
sorghum	0.17	0.17	0.12	0.14	0.11	0.06	0.04	0.02	0.05	0.10	0.17	0.05	0.09	0.00
bush_bean	0.14	0.17	0.17	0.17	0.16	0.19	0.04	0.18	0.15	0.03	0.08	0.10	0.18	0.14
maize	0.06	0.07	0.06	0.08	0.09	0.08	0.11	0.11	0.10	0.10	0.08	0.03	0.00	0.05
m3.Ca	825.38	808.59	892.40	829.36	938.66	801.09	731.73	692.21	447.86	551.92	604.23	813.76	556.78	544.69
m3.Mg	211.62	199.21	226.45	211.18	230.07	217.20	207.29	222.62	143.91	183.00	209.25	281.07	158.64	177.75
pH	5.47	5.50	5.49	5.46	5.59	5.48	5.41	5.42	5.06	5.21	5.33	5.46	5.27	5.19
Total.N	0.15	0.16	0.16	0.16	0.15	0.15	0.14	0.13	0.16	0.15	0.13	0.13	0.15	0.15
Total.C	2.07	2.10	2.09	2.09	2.17	2.05	2.04	1.88	2.33	2.11	1.92	1.92	2.06	2.10
ExAl	0.58	0.59	0.63	0.60	0.50	0.65	0.67	0.56	1.01	0.81	0.68	0.54	0.81	0.81
own.cows	0.69	0.63	0.58	0.74	0.71	0.69	0.82	0.70	0.90	0.87	0.75	0.64	0.64	0.81
own.goats	0.46	0.52	0.47	0.46	0.53	0.50	0.46	0.55	0.50	0.47	0.50	0.51	0.27	0.52
own.chickens	0.30	0.35	0.33	0.30	0.33	0.30	0.54	0.43	0.45	0.43	0.58	0.41	0.45	0.33
own.pigs	0.42	0.37	0.32	0.32	0.27	0.33	0.39	0.36	0.35	0.37	0.17	0.38	0.36	0.40
own.sheep	0.13	0.09	0.14	0.17	0.11	0.08	0.29	0.14	0.10	0.13	0.00	0.03	0.09	0.07
black_soil	0.54	0.54	0.55	0.56	0.49	0.43	0.54	0.45	0.55	0.50	0.25	0.62	0.45	0.62
red_soil	0.30	0.25	0.29	0.25	0.29	0.37	0.25	0.30	0.30	0.33	0.42	0.23	0.36	0.26
sandy_soil	0.13	0.19	0.14	0.15	0.18	0.18	0.07	0.18	0.05	0.07	0.17	0.13	0.18	0.10

3.8 Tenure balance

We’re defining Tubura tenure as having 3 or more seasons of experience farming with Tubura. We draw the line at 3 seasons as three seasons of fertilizer use is approximately when we’d expect fertilizer to start to have a detrimental effect on soil health.

oafOnly$tenured <- ifelse(oafOnly$total.seasons>=3,1,0)
tenure <- do.call(rbind, lapply(out.list, function(x) {
  
  out <- t.test(oafOnly[,x] ~ oafOnly[,"tenured"], data=oafOnly)
  tab <- data.frame(out[[5]][[1]], out[[5]][[2]], out[3])
  tab[,1:2] <- round(tab[,1:2],3)
  names(tab) <- c(names(out[[5]]), "pvalue")
  return(tab)
}))
# use p.adjust with bonferroni correction
tenure$pvalue <- p.adjust(tenure$pvalue, method="fdr")
rownames(tenure) <- out.list
tenure <- tenure[order(tenure$pvalue),]
tenure$pvalue <- ifelse(tenure[, 3] < 0.001, "< 0.001", round(tenure[, 3], 3)) 
colnames(tenure) <- c("Non-Tubura", "Tubura Client", "p-value") 

print(kable(tenure))

	Non-Tubura	Tubura Client	p-value
n_season_fert	2.109	4.762	< 0.001
n_season_compost	5.344	6.890	< 0.001
climbing_bean	0.161	0.295	< 0.001
slope	8.850	11.166	< 0.001
age	42.675	46.039	0.001
hillside	0.617	0.735	0.001
sorghum	0.172	0.086	0.001
n_seasons_leg_1	2.022	2.579	0.005
valley	0.320	0.226	0.008
cows	0.986	1.356	0.011
hhsize	5.205	5.599	0.012
female	0.459	0.551	0.018
alt	1509.106	1610.665	0.021
Total.N	0.155	0.149	0.059
own.cows	0.656	0.717	0.123
n_season_fallow	0.557	0.733	0.222
field.size	609.258	537.968	0.224
pH	5.485	5.420	0.224
own.pigs	0.388	0.336	0.224
hilltop	0.063	0.039	0.233
ExAl	0.585	0.645	0.235
m3.Mg	204.127	213.884	0.412
m3.Ca	815.244	767.659	0.421
sheep	0.205	0.262	0.474
own.chickens	0.328	0.360	0.474
sandy_soil	0.167	0.142	0.474
own.sheep	0.104	0.123	0.511
Total.C	2.090	2.065	0.574
red_soil	0.270	0.295	0.574
chickens	1.145	1.259	0.605
n_season_lime	0.227	0.259	0.616
maize	0.066	0.077	0.616
black_soil	0.538	0.519	0.662
n_seasons_leg_2	2.475	2.569	0.743
goats	1.060	1.103	0.751
own	0.940	0.945	0.788
bush_bean	0.158	0.151	0.788
pigs	0.533	0.548	0.844
own.goats	0.492	0.488	0.91

3.9 Tenured v. new balance interpretation

Demographic variables We are well balanced along demographic variables.

Agricultural practice variables Not surprisingly, Tubura farmers have more cumulative years of fertilizer use than current non-Tubura farmers. While that difference is signficant, it is realistically only a single season of fertilizer use different.

Interestingly, non-Tubura farmers reported using more lime than current Tubura farmers. This

Soil Variables Soil pH, calcium and magnesium levels are lower for tenured Tubura farmers. This is consistent with the hypothesis that increaesd fertilizer use leads to an increaese in soil acidity.

3.10 Analysis of agronomic practices contributing to soil health outcomes

Here’s where we’ll look at the contribution of fertilizer, lime and cultivation practices on soil health outcomes. This analysis will be come richer as we gain longitudinal measures. I caution that we cannot treat these relationships as causal. The direction of causality is not clearly delineated in the data or the study design. However, we can identify meaningful connections between practices and outcomes through this analysis to generate new hypotheses for field testing.

I’m going to start with behaviors by sections and then move to a more comprehensive model including multiple practices. All models will include controls for site to account for local variation and field officer behavior.

4 Analysis

4.1 Agronomic Behaviors

Check for multicollinearity before adding number of seasons of agronomic inputs on the same side of the regression.

suppressMessages(library(stargazer))
inputUse <- c("n_season_fert","n_season_compost", "n_season_lime", "n_season_fallow")
cor(d[,inputUse], use="complete.obs")

                 n_season_fert n_season_compost n_season_lime n_season_fallow
n_season_fert        1.0000000       0.35575631    0.17038250     -0.06419940
n_season_compost     0.3557563       1.00000000    0.03734895     -0.16878470
n_season_lime        0.1703825       0.03734895    1.00000000     -0.01112774
n_season_fallow     -0.0641994      -0.16878470   -0.01112774      1.00000000

Interpretation: The strongest correlation between the input use intensity variables is between seasons of fertilizer and compost use, ~0.35. While this is on the higher end it’s not necessarily cause for alarm.

inputUse <- paste(c("n_season_fert","n_season_compost", "n_season_lime", "n_season_fallow"), collapse= " + ")
list1 <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~", inputUse, "+ as.factor(cell)", sep="")), data=d)
  return(mod)
})
stargazer(list1, type="html", 
          title = "2016A Rwanda Soil Health Baseline - Naive Agronomic Practice Models",
          covariate.labels = c("Seasons of Fertilizer", "Seasons of Compost", 
                               "Seasons of Lime", "Seasons of Fallow"),
          dep.var.caption = "",
          dep.var.labels = "",
          column.labels = c(gsub("m3.","", soilVars)),
          notes = "Includes FE for cell",
          omit=c("cell"), out=paste("output", "rw_baseline_agprac.htm", sep="/"))

2016A Rwanda Soil Health Baseline - Naive Agronomic Practice Models

	Ca	Mg	pH	Total.N	Total.C	ExAl
	(1)	(2)	(3)	(4)	(5)	(6)
Seasons of Fertilizer	-3.601	-0.138	-0.002	-0.0001	-0.002	0.0003
	(4.042)	(0.776)	(0.004)	(0.0002)	(0.003)	(0.003)
Seasons of Compost	0.761	-0.007	0.004	0.00000	0.002	-0.005*
	(3.270)	(0.628)	(0.003)	(0.0002)	(0.003)	(0.003)
Seasons of Lime	42.291**	7.921**	0.011	0.002**	0.017	0.009
	(17.647)	(3.389)	(0.016)	(0.001)	(0.014)	(0.015)
Seasons of Fallow	-32.572***	-3.291***	-0.038***	0.001	0.007	0.025***
	(6.433)	(1.235)	(0.006)	(0.0004)	(0.005)	(0.005)
Constant	439.964	88.243	5.164***	0.210***	3.436***	1.762***
	(472.287)	(90.710)	(0.420)	(0.026)	(0.370)	(0.403)
Observations	2,439	2,439	2,439	2,439	2,439	2,439
R2	0.507	0.497	0.574	0.503	0.439	0.561
Adjusted R2	0.462	0.451	0.535	0.458	0.388	0.521
Residual Std. Error (df = 2234)	472.106	90.675	0.420	0.026	0.369	0.403
F Statistic (df = 204; 2234)	11.266***	10.817***	14.741***	11.083***	8.561***	13.994***
Note:	p<0.1; p<0.05; p<0.01
	Includes FE for cell

Interpretation The naive model suggests that when we include site level fixed effects, duration of agronomic practices don’t have a big effect on soil health outcomes. However, some of the practice intensity variables are not well distributed. Let’s take a look at a log transformation. I’m adding one to the variables as to not end up with lots of Inf values.

Log transformations in theory are appropriate for variables that are right skewed (vavlues clustered to the left of the distribution) and see diminishing returns to increasing values. The shape of the data suggests a log transformation but it’s debateable whether the relationship is diminishing.

agPrac <- c(names(d[grep('n_season_', names(d))]))
for(i in 1:length(agPrac)){
  print(
    ggplot(d, aes(x=d[,agPrac[i]])) + geom_density() +
      labs(x = paste(agPrac[i], " No transform", sep = ""))
        )
}

# since these are all skewed, consider a log transform 
for(i in 1:length(agPrac)){
  print(
    ggplot(d, aes(x=log10(d[,agPrac[i]]+1))) + geom_density() + 
      labs(x = paste(agPrac[i], " Log transform", sep = ""))
        )
}

# look at other transfomations
for(i in 1:length(agPrac)){
  print(
    ggplot(d, aes(x=d[,agPrac[i]]^(1/3))) + geom_density() +
      labs(x = paste(agPrac[i], " cubic root transform", sep = ""))
        )
}

# visualize the outcomes as well to see if a transformation is warranted
for(i in 1:length(soilVars)){
  print(
    ggplot(d, aes(x=d[,soilVars[i]])) + geom_density() +
      labs(x = soilVars[i], title = soilVars[i])
        )
}

d$logFert <- log(d$n_season_fert+1)
d$logCompost <- log(d$n_season_compost+1)
d$logLime <- log(d$n_season_lime+1)
d$logFallow <- log(d$n_season_fallow+1)

Or look at BoxCox graph to empirically determine the right transformation. Log is assuming a diminishing return to an increasing X. That’s probably not the case with fertilizer. We’d actually expect an increasing return as values get larger. We use boxcox to see what the data suggest. We interpret it as follows:

• lambda = 2 -> square
• lambda = 1 -> no transformation
• lambda = 0.5 -> square root
• lambda = 0 -> log
• lamdba = -1 -> inverse

library(MASS)
for(i in 1:length(agPrac)){
boxcox(lm(pH ~ d[,agPrac[i]], data=d))
}

For pH at least it seems like a log transform is appropriate. We can run this for all other variables as well to see what we get as well.

Let’s look at the log results: See here and here for guidance on intepreting log transformed right hand side variables. See here for additional guidance on choosing a transformation.

How to interpret RHS log transform: For a linear multivariate OLS regression, we say “a one unit increase in X causes a (coefficient) change in Y.” For a linear-log regression where the X variable is log transformed, we say a L percent change in X leads to a (coefficient*L) change in Y.

Question: Do we want to run this analysis for only OAF farmers? If so, adjust the sample accordingly.

logVars <- paste(names(d[grep("log", names(d))]), collapse=" + ")
list2 <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~",  logVars, "+ as.factor(cell)", sep="")), data=d)
  return(mod)
})
list2b <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~",  logVars, sep="")), data=d)
  return(mod)
})

suppressWarnings(
  stargazer(list2, list2b, type="html", 
          title = "2016A Rwanda Soil Health Baseline - Log Agronomic Practice Models",
          covariate.labels = c("Seasons of Fertilizer (log)", "Seasons of Compost (log)", "Seasons of Lime (log)", "Seasons of Fallow (log)"),
          column.labels = c(rep(gsub("m3.","", soilVars),2)),
          dep.var.caption = "",
          dep.var.labels = c("",""),
          add.lines = list(c("Cell FE?", rep("Yes", 5), rep("No", 5))),
          notes = "Includes FE for cell",
          omit=c("cell"), out=paste("output", "rw_baseline_agprac_log.htm", sep="/"))
)

plm.log <- function(x, range){
  
  beta = round(summary(x)$coefficients[range,1],3)
  beta.pval = summary(x)$coefficients[range,4]
  beta.conv = ifelse(beta.pval < 0.01, "***", ifelse(
    beta.pval < 0.05, "**", ifelse(
      beta.pval < 0.1, "*", "")))
  #beta.pval = round(beta.pval, 3)
  outcome = paste(beta, beta.conv, sep = "")
  outcome = c(outcome, unique(round(summary(x)$coefficients[1,1],3)))
  res = data.frame(outcome, stringsAsFactors = F)
  
  return(res)  
}
rw.table15 <- do.call(cbind, lapply(list2, function(x){
  plm.log(x, 2:5)
}))
colnames(rw.table15) <- soilVars
rownames(rw.table15) <- c(names(d)[grep("log", names(d))], "constant")

4.2 Table 15 outcomes

rw.table15 <- rw.table15[,c("pH", "Total.C", "Total.N", "m3.Ca", "m3.Mg", "ExAl")]
write.csv(rw.table15, file=paste("output", "rwtable15_reg.csv", sep = "/"),
          row.names = T)

# a 10 percent increase in x leads to B1*.1 change in Y
logTrans <- do.call(cbind, lapply(list2, function(x){
  coeff = x$coefficients[2:5]
  tenPercent = round(coeff*.1, 5)
  names(tenPercent) <- paste("10% increase in ", gsub("log","", names(tenPercent)), " leads to:", sep="")
  return(tenPercent)
}))
colnames(logTrans) <- soilVars

print(kable(logTrans))

	m3.Ca	m3.Mg	pH	Total.N	Total.C	ExAl
10% increase in Fert leads to:	-1.78758	-0.11370	-0.00150	-0.00004	-0.00089	0.00048
10% increase in Compost leads to:	0.22044	-0.02094	0.00129	-0.00002	0.00061	-0.00176
10% increase in Lime leads to:	14.37415	2.65234	0.00599	0.00074	0.00658	0.00030
10% increase in Fallow leads to:	-11.54532	-1.13393	-0.01315	0.00013	0.00177	0.00873

Interpretation: When we transform the variables to log, the data starts to tell a more coherent story, at least directionally. If we remove the district FE, the coefficients gain significance.

• Additional seasons of fertilizer use relates to a decrease in soil N and C.
• Seasons of compost and lime have the opposite, positive effect on soil N.
• Unsurprisingly, soil health metrics are poorer for soils on which more fertilizer has been used and lime has not been used.

4.2.1 Tenure with One Acre Fund

Let’s look first at a naive model of One Acre Fund tenure on soil health. Remember: these data are not longitudinal! These data are not longitudinal and reflect farmer selection into One Acre Fund. While these models will try to be both robust and parsimonious, we will inevitabily suffer omitted variable bias due to a lack of an instrument.

list3 <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~",  "total.seasons + as.factor(cell)", sep="")), data=d)
  return(mod)
})
stargazer(list3, type="html", 
          title = "2016A Rwanda Soil Health Baseline - Naive Tenure Models",
          covariate.labels = c("OAF Tenure"),
          dep.var.caption = "",
          dep.var.labels = "",
          column.labels = c(gsub("m3.","", soilVars)),
          notes = "Includes FE for cell",
          omit=c("cell"), out=paste("output", "rw_baseline_tenure.htm", sep="/"))

2016A Rwanda Soil Health Baseline - Naive Tenure Models

	Ca	Mg	pH	Total.N	Total.C	ExAl
	(1)	(2)	(3)	(4)	(5)	(6)
OAF Tenure	-0.129	0.745	0.001	-0.0002	-0.004	-0.002
	(3.471)	(0.663)	(0.003)	(0.0002)	(0.003)	(0.003)
Constant	443.267	86.727	5.178***	0.210***	3.451***	1.746***
	(475.253)	(90.857)	(0.424)	(0.026)	(0.369)	(0.406)
Observations	2,439	2,439	2,439	2,439	2,439	2,439
R2	0.500	0.494	0.564	0.501	0.438	0.556
Adjusted R2	0.455	0.449	0.525	0.457	0.388	0.516
Residual Std. Error (df = 2237)	475.202	90.847	0.424	0.026	0.369	0.405
F Statistic (df = 201; 2237)	11.127***	10.880***	14.408***	11.196***	8.684***	13.913***
Note:	p<0.1; p<0.05; p<0.01
	Includes FE for cell

Interpretation: The naive One Acre Fund tenure model suggest that across the board that additional years of 1AF practices have a negative effect on soil health parameters. Let’s combine 1AF tenure with the agronomic practices model above to build a more robust model:

list4 <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~",  logVars, "+ total.seasons + as.factor(cell)", sep="")), data=d)
  return(mod)
})
stargazer(list4, type="html", 
          title = "2016A Rwanda Soil Health Baseline - Ag Practice and Tenure",
          covariate.labels = c("Seasons of Fertilizer (log)", "Seasons of Compost (log)", "Seasons of Lime (log)", "Seasons of Fallow (log)", "OAF Tenure"),
          dep.var.caption = "",
          dep.var.labels = "",
          column.labels = c(gsub("m3.","", soilVars)),
          notes = "Includes FE for cell",
          omit=c("cell"), out=paste("output", "rw_baseline_ag_tenure.htm", sep="/"))

2016A Rwanda Soil Health Baseline - Ag Practice and Tenure

	Ca	Mg	pH	Total.N	Total.C	ExAl
	(1)	(2)	(3)	(4)	(5)	(6)
Seasons of Fertilizer (log)	-22.634	-3.440	-0.024	0.0003	0.002	0.014
	(16.929)	(3.258)	(0.015)	(0.001)	(0.013)	(0.014)
Seasons of Compost (log)	2.280	-0.173	0.013	-0.0003	0.006	-0.018
	(14.833)	(2.854)	(0.013)	(0.001)	(0.012)	(0.013)
Seasons of Lime (log)	143.969***	26.634***	0.060*	0.007***	0.065**	0.003
	(39.808)	(7.660)	(0.035)	(0.002)	(0.031)	(0.034)
Seasons of Fallow (log)	-115.872***	-11.542***	-0.132***	0.001	0.019	0.088***
	(18.857)	(3.629)	(0.017)	(0.001)	(0.015)	(0.016)
OAF Tenure	2.125	1.029	0.004	-0.0003	-0.005	-0.004
	(4.209)	(0.810)	(0.004)	(0.0002)	(0.003)	(0.004)
Constant	435.090	86.437	5.151***	0.211***	3.444***	1.779***
	(470.889)	(90.611)	(0.418)	(0.026)	(0.370)	(0.403)
Observations	2,439	2,439	2,439	2,439	2,439	2,439
R2	0.511	0.499	0.578	0.505	0.440	0.562
Adjusted R2	0.466	0.453	0.539	0.459	0.388	0.522
Residual Std. Error (df = 2233)	470.204	90.479	0.418	0.026	0.369	0.403
F Statistic (df = 205; 2233)	11.396***	10.863***	14.891***	11.092***	8.556***	14.004***
Note:	p<0.1; p<0.05; p<0.01
	Includes FE for cell

Interpretation: Including agronomic practices and 1AF tenure in the same model dampens the magnitude, but not the significance, of 1AF tenure on soil health outcomes.

4.3 Agronomic practices - 15B cultivation practices

Thus far we have looked at aggregated historical plot level practices and their effect on soil health. We also asked farmers about their cultivation practices on their plot in the previous season, 15B. We have more precise information for fertilizer, compost and liming practices for the 15B season.

# scale all the field application variables to ares
d$ares <- d$field.size/100
d$fert1.are <- d$field_kg_fert1_15b/d$ares
d$fert2.are <- d$field_kg_fert2_15b/d$ares
d$fert.total.are <- apply(d[,c("fert1.are", "fert2.are")], 1, function(x){
  sum(x, na.rm=T)
})
d$fert.total.are <- ifelse(is.na(d$fert1.are) & is.na(d$fert2.are), NA, d$fert.total.are)
d$compost.are <- d$field_kg_compost_15b/d$ares
d$lime.are <- d$kg_lime_15b/d$ares
intensityVars <- c("fert1.are", "fert2.are",
                   "compost.are", "lime.are")
cor(d[,intensityVars], use="complete.obs")

            fert1.are fert2.are compost.are  lime.are
fert1.are   1.0000000 0.9889407   0.5216134 0.8679373
fert2.are   0.9889407 1.0000000   0.5328874 0.8539114
compost.are 0.5216134 0.5328874   1.0000000 0.6236616
lime.are    0.8679373 0.8539114   0.6236616 1.0000000

#table(d$field_15b_fert_t, useNA = 'ifany')
d$field_15b_fert_t <- tolower(d$field_15b_fert_t)
names(d)[names(d)=="v69"] <- "field_15b_fert_t2"
#table(d$field_15b_fert_t2)
d$field_15b_fert_t2 <- tolower(d$field_15b_fert_t2)

d$dap1 <- ifelse(d$field_15b_fert_t=="dap", d$field_kg_fert1_15b, NA)
d$dap1.are <- d$dap1/d$ares
d$npk1 <- ifelse(grepl("npk", d$field_15b_fert_t), d$field_kg_fert1_15b, NA)
d$npk1.are <- d$npk1/d$ares
d$urea1 <- ifelse(d$field_15b_fert_t=="urea", d$field_kg_fert1_15b, NA)
d$urea1.are <- d$urea1/d$ares

d$urea2 <- ifelse(d$field_15b_fert_t2=="urea", d$field_kg_fert2_15b, NA)
d$urea2.are <- d$urea2/d$ares

d$total.urea.are <- apply(d[,c("urea1.are", "urea2.are")], 1, function(x){
  sum(x, na.rm=T)
})
d$total.urea.are <- ifelse(is.na(d$urea1.are) & is.na(d$urea2.are), NA, d$total.urea.are)

4.3.1 Graph the application/are variables

for(i in 1:length(intensityVars)){
  print(
  ggplot(d, aes(x=d[,intensityVars[i]])) + geom_density() +
      labs(x = intensityVars[i], title = intensityVars[i])
  )
}

Conclusion - Take 1: The application rate per are variables are weird. I think it’s because of the field dimensions. I’m going to go back to the field dimensions and check this.

Let’s look at the dimensions of the fields that have large application rates

d[d$fert1.are>10 & !is.na(d$fert1.are), c("field_dim1", "field_dim2", "field.size", "fert1.are")]

d[d$fert2.are>10 & !is.na(d$fert2.are), c("field_dim1", "field_dim2", "field.size", "fert2.are")]

d[d$compost.are>500 & !is.na(d$compost.are), c("field_dim1", "field_dim2", "field.size", "compost.are")]

# there's a field that is 1 meter wide? Surely not.
d[abs(d$lime.are)>40 & !is.na(d$lime.are), c("field_dim1", "field_dim2", "field.size", "lime.are")]

# how is there a negative quantity of lime?

4.4 Tenure Scatter plots

Generate tenure vs. pH and tenure vs. fertilizer seasons scatter plots:

tenure.ph <- ggplot(d, aes(x=jitter(total.seasons), y=pH)) + geom_point() + 
  geom_smooth(method="loess") +
  labs(x = "OAF Tenure", y = "Soil pH", title = "OAF Tenure vs. soil pH in Rwanda")
tenure.ph
pdf(paste("output", "rw tenure vs ph.pdf", sep = "/"), width=11, height=8.5)

print(tenure.ph)
dev.off()

quartz_off_screen 
                2 

tenure.fertilizer <- ggplot(d, aes(x= jitter(total.seasons), y=jitter(n_season_fert))) + geom_point() +
  geom_smooth(method='loess') + 
  labs(x = "OAF Tenure", y = "Years of Fertilizer in past 5 years", title = "OAF Tenure vs. soil pH in Rwanda")
tenure.fertilizer
pdf(paste("output", "rw tenure vs fertilizer.pdf", sep = "/"), width=11, height=8.5)

print(tenure.fertilizer)
dev.off()

quartz_off_screen 
                2 

4.5 Table 16 - Kgs of soil ammendments - fertilizer (kg)

# this should be kg of fertilizer used in this field. Compost is off the charts. Convert this to compost per sq meter
plm.t16 <- function(x, range){
  
  beta = round(summary(x)$coefficients[range,1],3)
  beta.pval = round(summary(x)$coefficients[range,4],3)
  beta.conv = ifelse(beta.pval < 0.01, "***", ifelse(
    beta.pval < 0.05, "**", ifelse(
      beta.pval < 0.1, "*", "")))
  #beta.pval = round(beta.pval, 3)
  outcome = paste(beta, " (", beta.pval, ")", sep = "")
  outcome = c(outcome, unique(round(summary(x)$coefficients[1,1],3)))
  res = data.frame(outcome, stringsAsFactors = F)
  
  return(res)  
}
previousSeasonfert <- paste("fert1.are", "as.factor(cell)", sep=" + ")
list5 <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~", previousSeasonfert, sep="")), data=d)
  return(mod)
})
table16 <- do.call(cbind, lapply(list5, function(x){
  plm.t16(x, 2)
}))
colnames(table16) <- soilVars
rownames(table16) <- c("Fertilizer (kg/are)", "constant")
table16 <- table16[,c("pH", "Total.C", "Total.N", "m3.Ca", "m3.Mg", "ExAl")]
write.csv(table16, file=paste("output", "rwtable16_fert.csv", sep = "/"),
          row.names = T)

4.6 Table 16 - all fertilizer used

previousSeasonfertAll <- paste("fert.total.are", "as.factor(cell)", sep=" + ")
list5a <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~", previousSeasonfertAll, sep="")), data=d)
  return(mod)
})
table16a <- do.call(cbind, lapply(list5a, function(x){
  plm.t16(x, 2)
}))
colnames(table16a) <- soilVars
rownames(table16a) <- c("Fertilizer (all) (kg/are)", "constant")
table16a <- table16a[,c("pH", "Total.C", "Total.N", "m3.Ca", "m3.Mg", "ExAl")]
write.csv(table16a, file=paste("output", "rwtable16_fert_all.csv", sep = "/"),
          row.names = T)

4.7 Table 16 - Kgs of soil ammendments - compost (kg)

# these objects have the same name as the fertilizer objects for simplicity. Run
# from the top to avoid overwriting issues.
previousSeasoncompost <- paste("compost.are", "as.factor(cell)", sep=" + ")
temp <- subset(d, d$compost.are>0)
list5b <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("temp[,x] ~", previousSeasoncompost, sep="")), data=temp)
  return(mod)
})
table16b <- do.call(cbind, lapply(list5b, function(x){
  plm.t16(x, 2)
}))
colnames(table16b) <- soilVars
rownames(table16b) <- c("Compost (kg/are)", "constant")
table16b <- table16b[,c("pH", "Total.C", "Total.N", "m3.Ca", "m3.Mg", "ExAl")]
write.csv(table16b, file=paste("output", "rwtable16_compost.csv", sep = "/"),
          row.names = T)

The compost regression results are suprising. Look at Carbon and compost scatter plot. If we include 0 compost, we see no relationship. If we exclude 0 compost, we

soilC.compost <- ggplot(temp, aes(x = compost.are, y=Total.C)) + geom_point() + 
  scale_x_continuous(limits=c(0,50)) + # remove larger values from the graph.
  geom_smooth(method='lm') +
  labs(title = "Compost and Soil Carbon in Rwanda", x = "Compost (kg/acre)", y = "Total Carbon")
soilC.compost
pdf(paste("output", "compost vs soil c.pdf", sep = "/"), width=11, height=8.5)

print(soilC.compost)
dev.off()

quartz_off_screen 
                2 

There are too many zero or near zero values. We can conclude that perhaps we’re not asking this question well. Even when we remove non-zero values, there’s little discernable relationship.

4.8 Table 16 - Kgs of soil ammendments - lime (kg)

# these objects have the same name as the fertilizer objects for simplicity. Run
# from the top to avoid overwriting issues.
previousSeasonlime <- paste("lime.are", "as.factor(cell)", sep=" + ")
list5c <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~", previousSeasonlime, sep="")), data=d)
  return(mod)
})
table16c <- do.call(cbind, lapply(list5c, function(x){
  plm.t16(x, 2)
}))
colnames(table16c) <- soilVars
rownames(table16c) <- c("Lime (kg/are)", "constant")
table16c <- table16c[,c("pH", "Total.C", "Total.N", "m3.Ca", "m3.Mg", "ExAl")]
write.csv(table16c, file=paste("output", "rwtable16_lime.csv", sep = "/"),
          row.names = T)

4.9 Table 16 - types of fertilizer - DAP (kg)

previousSeasonDAP <- paste("dap1.are", "as.factor(cell)", sep=" + ")
list5d <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~", previousSeasonDAP, sep="")), data=d)
  return(mod)
})
table16d <- do.call(cbind, lapply(list5d, function(x){
  plm.t16(x, 2)
}))
colnames(table16d) <- soilVars
rownames(table16d) <- c("DAP (kg/are)", "constant")
table16d <- table16d[,c("pH", "Total.C", "Total.N", "m3.Ca", "m3.Mg", "ExAl")]
write.csv(table16d, file=paste("output", "rwtable16_dap.csv", sep = "/"),
          row.names = T)

4.10 Table 16 - types of fertilizer - NPK (kg)

previousSeasonNPK <- paste("npk1.are", "as.factor(cell)", sep=" + ")
list5e <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~", previousSeasonNPK, sep="")), data=d)
  return(mod)
})
table16e <- do.call(cbind, lapply(list5e, function(x){
  plm.t16(x, 2)
}))
colnames(table16e) <- soilVars
rownames(table16e) <- c("NPK (kg/are)", "constant")
table16e <- table16e[,c("pH", "Total.C", "Total.N", "m3.Ca", "m3.Mg", "ExAl")]
write.csv(table16e, file=paste("output", "rwtable16_npk.csv", sep = "/"),
          row.names = T)

4.11 Table 16 - types of fertilizer - Urea (kg)

previousSeasonUrea <- paste("total.urea.are", "as.factor(cell)", sep=" + ")
list5f <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~", previousSeasonUrea, sep="")), data=d)
  return(mod)
})
table16f <- do.call(cbind, lapply(list5f, function(x){
  plm.t16(x, 2)
}))
colnames(table16f) <- soilVars
rownames(table16f) <- c("Urea (kg/are)", "constant")
table16f <- table16f[,c("pH", "Total.C", "Total.N", "m3.Ca", "m3.Mg", "ExAl")]
write.csv(table16f, file=paste("output", "rwtable16_urea.csv", sep = "/"),
          row.names = T)

stargazer(list5, type="html", 
          title = "2016A Rwanda Soil Health Baseline - 15B practices",
          covariate.labels = c("Fertilizer Rate (log)"),
          dep.var.caption = "",
          dep.var.labels = "",
          column.labels = c(gsub("m3.","", soilVars)),
          notes = "Includes FE for cell",
          omit=c("cell"), out=paste("output", "rw_baseline_15b_ag.htm", sep="/"))

4.12 Farmer fertility perception

Let’s look at farmer perceived fertility as a predictor of soil health. We’ll set ‘same fertility’ as the reference category.

d$fertility_qual <- relevel(as.factor(d$general_field_infocompare_fertil), ref="same")
list6 <- lapply(soilVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~", "+ fertility_qual + as.factor(cell)", sep="")), data=d)
  return(mod)
})
stargazer(list6, type="html", 
          title = "2016A Rwanda Soil Health Baseline - Farmer Perceived Fertility",
          covariate.labels = c("Farmer Opinion - Less Fertile",
                               "Farmer Opinion - More Fertile"),
          dep.var.caption = "",
          dep.var.labels = "",
          column.labels = c(gsub("m3.","", soilVars)),
          notes = "Reference category is same fertility as other fields. Includes FE for cell",
          notes.align = "l",
          omit=c("cell"), out=paste("output", "rw_baseline_fertility_qual.htm", sep="/"))

2016A Rwanda Soil Health Baseline - Farmer Perceived Fertility

	Ca	Mg	pH	Total.N	Total.C	ExAl
	(1)	(2)	(3)	(4)	(5)	(6)
Farmer Opinion - Less Fertile	-128.212***	-17.537***	-0.134***	0.002	0.013	0.116***
	(26.465)	(5.077)	(0.024)	(0.001)	(0.021)	(0.023)
Farmer Opinion - More Fertile	22.239	1.717	0.014	-0.001	-0.009	-0.017
	(29.278)	(5.617)	(0.026)	(0.002)	(0.023)	(0.025)
Constant	571.222	105.753	5.314***	0.208***	3.430***	1.626***
	(473.062)	(90.758)	(0.421)	(0.026)	(0.370)	(0.403)
Observations	2,439	2,439	2,439	2,439	2,439	2,439
R2	0.506	0.497	0.571	0.502	0.438	0.562
Adjusted R2	0.462	0.452	0.533	0.457	0.387	0.522
Residual Std. Error (df = 2236)	472.321	90.616	0.421	0.026	0.370	0.403
F Statistic (df = 202; 2236)	11.348***	10.943***	14.756***	11.160***	8.628***	14.179***
Note:	p<0.1; p<0.05; p<0.01
	Reference category is same fertility as other fields. Includes FE for cell

Interpretation: Farmers understand their fields well. Their categorization of which field are more and less fertile corresponds to our quantified measures of soil health. The only features farmers don’t seem to get correct are nitrogen and carbon. The nitrogen and carbon levels are indistinguishable in ‘low fertility’ fields relative to the fields deemed to be the ‘same fertility.’ Reminder: We need to remember that farmers are only evaluting one of their fields thus we are not able to account for the quality of the farmer in assessing his/her fields.

4.12.1 Deeper dive into farmer perception of fertility

• Look at wet chem values for all soil features by farmer perception
• Run PCA on all soil attributes to see which principle components predict soil fertility
• Consider adding additional control variables into model (like Ca as control for pH perception model)

# merge wetChem in with d
names(wetChem)[2:21] <- paste("wet.", names(wetChem)[2:21], sep = "")
d <- left_join(d, wetChem, by="SSN")

joining factor and character vector, coercing into character vector

4.12.2 For Eric, CEC summary for new Ca and Mg thresholds

ggplot(d, aes(x=wet.C.E.C)) + geom_density() + 
  geom_vline(xintercept = median(d$wet.C.E.C, na.rm=T), color="red") + 
  geom_vline(xintercept = mean(d$wet.C.E.C, na.rm=T), color="blue")

summary(d$wet.C.E.C)[3:4]

Median   Mean 
 8.620  9.684 

wetVars <- names(d)[grep("wet.", names(d))]
for(i in 1:length(wetVars)){
  print(
  ggplot(data=d, aes(x=as.factor(client), y=d[,wetVars[i]])) + 
    geom_boxplot() +
    labs(x="Tubura Farmer", y=wetVars[i], title = paste("RW baseline wet chem - ", wetVars[i], sep = ""))
  )
}

list7 <- lapply(wetVars, function(x){
  mod <- lm(as.formula(paste("d[,x] ~", "+ fertility_qual + as.factor(cell)", sep="")), data=d)
  return(mod)
})
suppressWarnings(
stargazer(list7, type="html", 
          title = "2016A Rwanda Soil Health Baseline - Farmer Perceived Fertility (wet chem)",
          covariate.labels = c("Farmer Opinion - Less Fertile",
                               "Farmer Opinion - More Fertile"),
          dep.var.caption = "",
          dep.var.labels = "",
          column.labels = c(gsub("wet.","", wetVars)),
          notes = "Reference category is same fertility as other fields. Includes FE for cell",
          notes.align = "l",
          omit=c("cell"), out=paste("output", "rw_baseline_fertility_qual_wet.htm", sep="/"))
)

2016A Rwanda Soil Health Baseline - Farmer Perceived Fertility (wet chem)

	pH	EC..S.	P	K	Ca	Mg	S	Na	Fe	Mn	B	Cu	Zn	C.E.C	TN	C.N	Exch..Acidity	Acid.Saturation	Exch.Al	OC
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)	(19)	(20)
Farmer Opinion - Less Fertile	-0.228	-5.566	-8.792	-55.794*	-74.347	-35.860	2.483	-1.881	19.090	-33.558	-0.075	-0.424	-0.334	-0.499	0.001	-0.201	0.385**	10.942***	0.302**	0.063
	(0.149)	(11.230)	(19.805)	(28.523)	(150.909)	(36.429)	(2.272)	(2.951)	(17.447)	(21.295)	(0.225)	(0.339)	(0.772)	(1.119)	(0.008)	(0.632)	(0.163)	(3.777)	(0.126)	(0.137)
Farmer Opinion - More Fertile	-0.112	-11.733	-11.765	-80.863**	-174.963	-71.564	2.687	-1.391	-13.159	-11.052	-0.061	-0.250	-0.880	-1.523	-0.023**	0.318	0.157	4.668	0.119	-0.277
	(0.186)	(14.042)	(25.228)	(36.333)	(192.230)	(46.404)	(2.895)	(3.759)	(22.224)	(27.126)	(0.286)	(0.432)	(0.983)	(1.425)	(0.010)	(0.806)	(0.208)	(4.811)	(0.161)	(0.174)
Constant	6.225***	127.000***	8.910	213.000***	1,745.000***	435.000***	15.700**	24.300***	61.450	194.000***	0.415	2.670***	4.415**	15.650***	0.195***	11.750***	0.140	1.100	0.023	2.335***
	(0.409)	(30.862)	(55.804)	(80.369)	(425.217)	(102.647)	(6.403)	(8.315)	(49.160)	(60.004)	(0.633)	(0.957)	(2.175)	(3.152)	(0.022)	(1.782)	(0.459)	(10.642)	(0.356)	(0.385)
Observations	234	234	241	241	241	241	241	241	241	241	241	241	241	241	241	241	241	241	241	241
R2	0.735	0.594	0.705	0.661	0.750	0.761	0.727	0.706	0.762	0.695	0.525	0.695	0.540	0.751	0.792	0.670	0.769	0.769	0.793	0.733
Adjusted R2	0.407	0.091	0.350	0.254	0.450	0.473	0.398	0.353	0.476	0.330	-0.046	0.328	-0.013	0.451	0.541	0.274	0.491	0.492	0.544	0.412
Residual Std. Error	0.579 (df = 104)	43.645 (df = 104)	78.919 (df = 109)	113.659 (df = 109)	601.348 (df = 109)	145.165 (df = 109)	9.055 (df = 109)	11.760 (df = 109)	69.523 (df = 109)	84.858 (df = 109)	0.895 (df = 109)	1.353 (df = 109)	3.077 (df = 109)	4.458 (df = 109)	0.032 (df = 109)	2.520 (df = 109)	0.649 (df = 109)	15.049 (df = 109)	0.503 (df = 109)	0.544 (df = 109)
F Statistic	2.238*** (df = 129; 104)	1.180 (df = 129; 104)	1.985*** (df = 131; 109)	1.625*** (df = 131; 109)	2.498*** (df = 131; 109)	2.642*** (df = 131; 109)	2.214*** (df = 131; 109)	2.001*** (df = 131; 109)	2.661*** (df = 131; 109)	1.900*** (df = 131; 109)	0.920 (df = 131; 109)	1.894*** (df = 131; 109)	0.976 (df = 131; 109)	2.503*** (df = 131; 109)	3.160*** (df = 131; 109)	1.691*** (df = 131; 109)	2.765*** (df = 131; 109)	2.772*** (df = 131; 109)	3.189*** (df = 131; 109)	2.283*** (df = 131; 109)
Note:	p<0.1; p<0.05; p<0.01
	Reference category is same fertility as other fields. Includes FE for cell

Interpretation: Our sample size decreases considerably when we look only at wet chemistry results. Thus, we do not see the same signficant relationships we saw between farmer perceived fertility and soil characteristics we saw when looking at the predicted values.

4.13 Principal Component Analysis (predicted values)

pca1 <- prcomp(d[,soilVars], scale=TRUE, center=TRUE)
print(pca1)

Standard deviations:
[1] 1.7694781 1.4197211 0.5985909 0.4836832 0.4092214 0.3059685

Rotation:
                 PC1          PC2           PC3        PC4         PC5         PC6
m3.Ca    0.497746227 -0.233189315  0.0200217427 -0.5908066 -0.05845757  0.58736772
m3.Mg    0.477406109 -0.130704228 -0.8045486852  0.2566894 -0.09624119 -0.18041456
pH       0.531809652  0.007760003  0.3663922355 -0.2518775  0.19398670 -0.69411952
Total.N  0.005638237 -0.663812926  0.2859668476  0.2266671 -0.64277520 -0.11404215
Total.C -0.100238773 -0.658444962 -0.0007406172  0.1625259  0.72558685  0.05925313
ExAl    -0.481072753 -0.232984039 -0.3691607865 -0.6662006 -0.10026596 -0.35232273

plot(pca1)

pca1$rotation

                 PC1          PC2           PC3        PC4         PC5         PC6
m3.Ca    0.497746227 -0.233189315  0.0200217427 -0.5908066 -0.05845757  0.58736772
m3.Mg    0.477406109 -0.130704228 -0.8045486852  0.2566894 -0.09624119 -0.18041456
pH       0.531809652  0.007760003  0.3663922355 -0.2518775  0.19398670 -0.69411952
Total.N  0.005638237 -0.663812926  0.2859668476  0.2266671 -0.64277520 -0.11404215
Total.C -0.100238773 -0.658444962 -0.0007406172  0.1625259  0.72558685  0.05925313
ExAl    -0.481072753 -0.232984039 -0.3691607865 -0.6662006 -0.10026596 -0.35232273

The first principal component is composed primarily of soil pH related variables, Ca, Mg, and pH. The second capture N and C. These groupings (pH grouping and C and N) are not all that surprising given that our predicted soil variable set is fairly limited.

If the variables have the same sign that indicates that they are positively correlated in the principal component. In the first principal component, we see Ca, Mg and pH loading in the same direction. In the second principal component, we see N and C moving in the same direction. Ca and Mg are also associated in the same direction but to a lesser extent.

ggplot(as.data.frame(pca1$x),aes(x=PC1,y=PC2, color=d$fertility_qual)) + geom_point() + 
  labs(title = "PCA of soil attributes and farmer perceived soil fertility",
       x = "First PCA", y= "Second PCA", color="Field Quality")

When we layer farmer perceived soil fertility on top of the principal component scatter plot, no clear pattern emergres. Fields with the same fertilitiy, less and more fertility are indistinguishable by the principal components. Thus, there is not a clear profile for farmer identified healthier soil or weaker soil based on principal components.

4.13.1 Geographic Descriptors - PCA for soil profiles

This section will build on the principal component work above and look at improving understanding of local context to inform local adaptation. Here we will also construct soil profiles to simplify the scaling of promising products and practices to targeted locations. We don’t have a full suite of predictors so we can’t look at a comprehensive soil profile.

ggplot(as.data.frame(pca1$x),aes(x=PC1,y=PC2, color=d$district)) + geom_point() + 
  labs(title = "PCA of soil attributes and district",
       x = "First PCA", y= "Second PCA", color="District")

When we color the figure by district, we start to see a pattern emerge. However, there are too many points to clearly detect where all districts fall. Let’s instead look at the data by AEZ.

ggplot(as.data.frame(pca1$x),aes(x=PC1,y=PC2, color=d$aez)) + geom_point() + 
  labs(title = "PCA of soil attributes and AEZ",
       x = "First PCA - Ca/Mg/pH", y= "Second PCA - N and C", color="AEZ")

Coloring the points by AEZ, we see a much clearer trend. The east is to the right of our graphic, then the central plateau followed by lake Kive and then Congo Nile in green on the left. What does this mean in terms of actual soil features? Let’s look at the figure but with the variable associations layered on top. See here for documentation

library(pca3d)
pca2d( pca1, biplot= TRUE, shape= 19, col= "black"  )

It appears that the right side of the graph, the eastern AEZ, is associated with pH, Mg and Ca. This indicates that as the first principal component increases, so does the level of pH, Ca and Mg. Conversely, total N and C increase as the second principal component decreases. In terms of the AEZ figure above, this suggest that the Congo Nile AEZ has higher levels of N and C. Let’s test these hypotheses with a simple summary table:

print(kable(aggregate(d[,soilVars], by=list(d$aez), function(x){
  round(mean(x, na.rm=T),5)
})))

Group.1	m3.Ca	m3.Mg	pH	Total.N	Total.C	ExAl
Central Plateau	793.5114	207.0087	5.53865	0.13754	1.87063	0.43171
Congo Nile	396.7151	125.7663	4.96555	0.16072	2.30810	1.14672
East	1341.5413	274.0356	5.93730	0.17050	2.12779	0.23845
Lake Kivu	742.6964	229.2307	5.47636	0.13980	2.01218	0.56104

Confirmed! This likely also suggests that Congo Nile is at a higher altitude than the surrounding areas and that eastern Rwanda has relatively less weathered soils compared to western.

Consequence for trial placement: The Rwanda program is already blocking trials by AEZ but these data confirm that AEZ reflects meaningfu soil variation and thus captures key growing conditions for Rwandan farmers. Blocking trials by AEZ in Rwanda will enable us to evaluate trial hypotheses in more neutral pH ranges and higher N and C conditions.

4.13.2 Principal Component Analysis (wet chemistry values)

wetVal <- d[complete.cases(d[,wetVars]),]
pca2 <- prcomp(wetVal[,c("wet.C.E.C", "wet.pH", "wet.Mg", "wet.Ca")], center=TRUE, scale=TRUE)
pca2 <- prcomp(wetVal[, wetVars], center=TRUE, scale=TRUE)

#plot(pca2)
ggplot(as.data.frame(pca2$x),aes(x=PC1,y=PC2, color=as.factor(wetVal$aez))) + geom_point() +
  labs(title = "Wet Chem PCA with AEZ", x = "First PC", y="Second PC",
       color="AEZ")

#pca2$rotation

pca2d(pca2, biplot= TRUE, shape= 19, col= "black")

# put pca2$x PC1 in the main data to run the fertility perception model.
#mod8 <- lm(
suppressWarnings(
stargazer(list7, type="html", 
          title = "2016A Rwanda Soil Health Baseline - Farmer Perceived Fertility (wet chem)",
          covariate.labels = c("Farmer Opinion - Less Fertile",
                               "Farmer Opinion - More Fertile"),
          dep.var.caption = "",
          dep.var.labels = "",
          column.labels = c(gsub("wet.","", wetVars)),
          notes = "Reference category is same fertility as other fields. Includes FE for cell",
          notes.align = "l",
          omit=c("cell"), out=paste("output", "rw_baseline_fertility_qual_wet.htm", sep="/"))
)

2016A Rwanda Soil Health Baseline - Farmer Perceived Fertility (wet chem)

	pH	EC..S.	P	K	Ca	Mg	S	Na	Fe	Mn	B	Cu	Zn	C.E.C	TN	C.N	Exch..Acidity	Acid.Saturation	Exch.Al	OC
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)	(19)	(20)
Farmer Opinion - Less Fertile	-0.228	-5.566	-8.792	-55.794*	-74.347	-35.860	2.483	-1.881	19.090	-33.558	-0.075	-0.424	-0.334	-0.499	0.001	-0.201	0.385**	10.942***	0.302**	0.063
	(0.149)	(11.230)	(19.805)	(28.523)	(150.909)	(36.429)	(2.272)	(2.951)	(17.447)	(21.295)	(0.225)	(0.339)	(0.772)	(1.119)	(0.008)	(0.632)	(0.163)	(3.777)	(0.126)	(0.137)
Farmer Opinion - More Fertile	-0.112	-11.733	-11.765	-80.863**	-174.963	-71.564	2.687	-1.391	-13.159	-11.052	-0.061	-0.250	-0.880	-1.523	-0.023**	0.318	0.157	4.668	0.119	-0.277
	(0.186)	(14.042)	(25.228)	(36.333)	(192.230)	(46.404)	(2.895)	(3.759)	(22.224)	(27.126)	(0.286)	(0.432)	(0.983)	(1.425)	(0.010)	(0.806)	(0.208)	(4.811)	(0.161)	(0.174)
Constant	6.225***	127.000***	8.910	213.000***	1,745.000***	435.000***	15.700**	24.300***	61.450	194.000***	0.415	2.670***	4.415**	15.650***	0.195***	11.750***	0.140	1.100	0.023	2.335***
	(0.409)	(30.862)	(55.804)	(80.369)	(425.217)	(102.647)	(6.403)	(8.315)	(49.160)	(60.004)	(0.633)	(0.957)	(2.175)	(3.152)	(0.022)	(1.782)	(0.459)	(10.642)	(0.356)	(0.385)
Observations	234	234	241	241	241	241	241	241	241	241	241	241	241	241	241	241	241	241	241	241
R2	0.735	0.594	0.705	0.661	0.750	0.761	0.727	0.706	0.762	0.695	0.525	0.695	0.540	0.751	0.792	0.670	0.769	0.769	0.793	0.733
Adjusted R2	0.407	0.091	0.350	0.254	0.450	0.473	0.398	0.353	0.476	0.330	-0.046	0.328	-0.013	0.451	0.541	0.274	0.491	0.492	0.544	0.412
Residual Std. Error	0.579 (df = 104)	43.645 (df = 104)	78.919 (df = 109)	113.659 (df = 109)	601.348 (df = 109)	145.165 (df = 109)	9.055 (df = 109)	11.760 (df = 109)	69.523 (df = 109)	84.858 (df = 109)	0.895 (df = 109)	1.353 (df = 109)	3.077 (df = 109)	4.458 (df = 109)	0.032 (df = 109)	2.520 (df = 109)	0.649 (df = 109)	15.049 (df = 109)	0.503 (df = 109)	0.544 (df = 109)
F Statistic	2.238*** (df = 129; 104)	1.180 (df = 129; 104)	1.985*** (df = 131; 109)	1.625*** (df = 131; 109)	2.498*** (df = 131; 109)	2.642*** (df = 131; 109)	2.214*** (df = 131; 109)	2.001*** (df = 131; 109)	2.661*** (df = 131; 109)	1.900*** (df = 131; 109)	0.920 (df = 131; 109)	1.894*** (df = 131; 109)	0.976 (df = 131; 109)	2.503*** (df = 131; 109)	3.160*** (df = 131; 109)	1.691*** (df = 131; 109)	2.765*** (df = 131; 109)	2.772*** (df = 131; 109)	3.189*** (df = 131; 109)	2.283*** (df = 131; 109)
Note:	p<0.1; p<0.05; p<0.01
	Reference category is same fertility as other fields. Includes FE for cell

4.14 District and cell level summaries of soil and managment practices

dist.sum <- aggregate(d[,out.list], by=list(d$district), function(x){
  return(c(
    paste(
      round(mean(x, na.rm=T),3), " (", round(median(x, na.rm=T),3), ")", 
      " (", round(sd(x, na.rm=T),2), ")", sep = ""
    )
    ))
})

write.csv(dist.sum, file=paste("output", "district covariate summary.csv", sep = "/"))

cell.sum <- aggregate(d[,out.list], by=list(d$cell), function(x){
  return(c(
    paste(
      round(mean(x, na.rm=T),3), " (", round(median(x, na.rm=T),3), ")", 
      " (", round(sd(x, na.rm=T),2), ")", sep = ""
    )
    ))
})

write.csv(cell.sum, file=paste("output", "cell covariate summary.csv", sep = "/"))

4.15 Female farmers farming poorer soils?

remove <- "female"
genderBalance <- out.list[!out.list %in% remove]
equal <- do.call(rbind, lapply(genderBalance, function(x){
    
    out <- t.test(d[,x] ~ d[,"female"], data=d)
    tab <- data.frame(out[[5]][[2]],out[[5]][[1]], out[3])
    tab[,1:2] <- round(tab[,1:2],3)
    names(tab) <- c(names(out[[5]]), "pvalue")
    #tab[,3] <- p.adjust(tab[,3], method="holm")
    #tab[,3] <- ifelse(tab[,3] < 0.001, "< 0.001", round(tab[,3],3))
    #print(tab)
    return(tab)
  
}))
rownames(equal) <- NULL
# order variables 
equal$pvalue <- p.adjust(equal$pvalue, method="fdr")
rownames(equal) <- genderBalance
equal <- equal[order(equal$pvalue),]
equal$pvalue <- ifelse(equal$pvalue < 0.001, "< 0.001", round(equal$pvalue,3))
colnames(equal) <- c("Male Farmers","Female Farmers", "p-value")    

write.csv(equal, file=paste("output", "rw female farmer status.csv", sep = "/"), row.names=T)

4.16 Export the data for Eric

write.csv(d, file=paste("output", "rwanda_shs_merged_clean.csv",sep = "/"), row.names = F)

5 Propensity Score Matching

We need to do a more rigorous job of accounting for differences between Tubura farmers and identified control farmers. Execute propensity score matching (PSM) to identify control farmers that overlap with Tubura farmers with regard to their likelihood of being a Tubura farmer.

psmVars <- paste(c("female", "age", "hhsize", "total.seasons",
                   "cows", "goats", "chickens", "pigs", "sheep"),
                   collapse=" + ")
reg <- glm(as.formula(paste("client ~", psmVars, sep="")), 
           family= binomial(link="logit"), data=d)
summary(reg)    

Call:
glm(formula = as.formula(paste("client ~", psmVars, sep = "")), 
    family = binomial(link = "logit"), data = d)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.5170  -0.8509   0.0426   0.9319   2.0723  

Coefficients:
               Estimate Std. Error z value Pr(>|z|)    
(Intercept)    0.413913   0.202863   2.040   0.0413 *  
female        -0.744249   0.097219  -7.655 1.93e-14 ***
age           -0.024071   0.003368  -7.147 8.90e-13 ***
hhsize         0.036271   0.022919   1.583   0.1135    
total.seasons  0.519325   0.028386  18.295  < 2e-16 ***
cows           0.006420   0.017843   0.360   0.7190    
goats         -0.004032   0.029835  -0.135   0.8925    
chickens       0.021716   0.017745   1.224   0.2211    
pigs           0.051552   0.057656   0.894   0.3713    
sheep          0.047237   0.069853   0.676   0.4989    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 3381.1  on 2438  degrees of freedom
Residual deviance: 2573.8  on 2429  degrees of freedom
AIC: 2593.8

Number of Fisher Scoring iterations: 5

# summarize predicted probabilities
pr <- data.frame(pr_score = predict(reg, type='response'), treat = d$client)
# graph
psmGraph <- ggplot() + geom_histogram(data=subset(pr, pr$treat==1), aes(x = pr_score, y=..count.., fill=as.factor(treat)), bins=80, position = "identity") +
    geom_histogram(data=subset(pr, pr$treat==0), aes(x=pr_score, y=-..count.., fill=as.factor(treat)), bins=80, position = "identity") +
    scale_y_continuous(limits=c(-150,150)) + 
  labs(title ="PSM score overlap", x = "PSM score", y="Farmer count",
       fill="Tubura/Control")
print(psmGraph)
pdf(file=paste("output", "rw_baseline_psm_overlap.pdf", sep="/"), height=8.5, width=11)

print(psmGraph)
dev.off()

quartz_off_screen 
                2 

Interpretation We have some overlap but it’s clear that Tubura farmers occupy a different range than the identified control farmers. Let’s continue with the PSM matching process but restrict ourselves to Tubura and control farmers that meet a certain PSM matching radius.

5.0.1 Notes on PSM process:

We have to indicate a variable for matching. I’m choosing pH as we know it to be an issue in most of our operating areas and addressing soil acidity has numerous residual benefits to soil health. I’ll want to do this for all soil outcomes, however.

• As a next step, I’d like to identify the matched subset and then regress Tubura tenure on soil outcomes. Does that make sense?
• I chose a caliper of 0.25. I should review this process with Maya to make certain I’m following best practice. I sort of just pulled that sd figure out of the sky.
• An initial check of the match quality using a common model indicates that the matches are poor.

# PSM prep
tr <- cbind(d$client)
x <- d[, unlist(strsplit(psmVars, " + ", fixed=T))]
y <- soilVars
# PSM
set.seed(20161102)
m <- lapply(y, function(response){
  suppressWarnings(
  mod <- Match(Y = d[,response], Tr = tr, X = reg$fitted, ties=FALSE, replace=FALSE, caliper=0.25, estimand = "ATE")    
  )
  matchRes <- MatchBalance(tr ~ d[,response], match.out=mod, nboots=500, data=d, print.level = 0)
  return(list(mod, matchRes))
})
#lapply(m, summary)

Now check the naive model approach for PSM balance.

matchRes <- do.call(rbind, lapply(1:length(m), function(model){
  val <- as.data.frame(cbind(
    standard.diff=m[[model]][[2]]$AfterMatching[[1]]$sdiff, 
    var.ratio = m[[model]][[2]]$AfterMatching[[1]]$var.ratio,
    sdiff.adj = m[[model]][[2]]$AfterMatching[[1]]$sdiff/100))
  return(val)
}))
rownames(matchRes) <- y

print(kable(matchRes))

	standard.diff	var.ratio	sdiff.adj
m3.Ca	-2.9586010	0.8621133	-0.0295860
m3.Mg	-9.8036266	0.7586184	-0.0980363
pH	0.9623464	0.9756746	0.0096235
Total.N	6.2212453	1.0317110	0.0622125
Total.C	0.7591351	1.0076355	0.0075914
ExAl	-1.2303827	0.9764596	-0.0123038

Interpretation: We want to see standard mean differences less than the absolute value of 0.25 (or 0.1 if we’re being conservative) and variance ratios close to 1 but certainly between 0.5 and 2.

According to the CRAN summary, sdiff is the standardized difference between the treatment and control units multiplied by 100. If I divide by 100, the values come much closer to reasonable value.

5.0.2 Let the PSM models vary

The common model approach doesn’t seem to be working for any of the variables. I’m going to rework the modeling approach so we can fit different models for each outcome upon which we’re trying to match.

d$age2 <- d$age^2
d$hhsize_age <- d$hhsize*d$age
d$hhsize2 <- d$hhsize^2
coreVars = c("female", "age", "hhsize", "own", "as.factor(cell)", "cows", "goats", "chickens", "pigs", "sheep", "black_soil", "red_soil", "sandy_soil")
psmList <- list(
  list(tr = "client",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="m3.Ca"),
  list(tr = "client",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="m3.Mg"),
  list(tr = "client",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="pH"),
  list(tr = "client",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="Total.N"),
  list(tr = "client",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="Total.C"),
  list(tr = "client",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="ExAl"),
  list(tr = "client",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="n_season_fert"),
  list(tr = "client",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="n_season_compost"),
  list(tr = "client",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="n_seasons_leg_1"),
  list(tr = "client",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="n_season_fallow"),
  list(tr = "client",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="logFert"),
  list(tr = "client",
       psmVars = paste(c(coreVars, "age2",
                   "hhsize2"),
                   collapse=" + "),
       y="n_seasons_leg_2"),
  list(tr = "client",
       psmVars = paste(c(coreVars, "age2",
                   "hhsize2"),
                   collapse=" + "),
       y="n_season_lime"),
  list(tr = "client",
       psmVars = paste(c("female", "age", "hhsize", "own",
                         "cows", "goats", "chickens", "pigs", "sheep",
                         "as.factor(district)", "age2", "hhsize2",
                         "hhsize_age"),
                   collapse=" + "),
       y="fert1.are"),
  # list(tr = "client",
  #      psmVars = paste(c("female", "age", "hhsize","as.factor(district)",
  #                  "cows", "goats", "chickens", "pigs", "sheep","age2", "hhsize2",
  #                        "hhsize_age"),
  #                  collapse=" + "),
  #      y="fert2.are"),
  list(tr = "client",
       psmVars = paste(c("female", "age", "hhsize","as.factor(district)",
                   "cows", "goats", "chickens", "pigs", "sheep","age2", "hhsize2",
                         "hhsize_age"),
                   collapse=" + "),
       y="compost.are")
  
)
# fertilizer application in 15B has missing values at the cell level so i include
# a district level control instead. We don't get a good fit with the current model.
# adjust!
# aggregate(d$fert1.are, by=list(d$client, d$cell), FUN=mean, na.rm=T)
# lime.are is also being finicky
# PSM
set.seed(20161102)
m <- lapply(psmList, function(listInput){
  naCheck <- unlist(strsplit(gsub("\\+", "", as.vector(listInput$psmVars))," ", fixed=TRUE))
  naCheck <- naCheck[-which(naCheck=="")]
  
  # keep complete cases of outcome variable
  k <- d[complete.cases(d[,listInput$y]),]
  k <- k[complete.cases(k[,listInput$y]),]
  
  # run glm regression:
  reg <- glm(as.formula(paste(listInput$tr, "~", listInput$psmVars, sep="")),  family= binomial(link="logit"), data=k)
  
  suppressWarnings(
  mod <- Match(Y = k[,listInput$y], Tr = k[,listInput$tr], X = reg$fitted, ties=FALSE, replace=FALSE, caliper=0.25, estimand = "ATE")   
  )
  matchRes <- MatchBalance(k[,listInput$tr] ~ k[,listInput$y], match.out=mod, nboots=500, data=k, print.level = 0)
  #print(listInput$y)
  return(list(mod, matchRes))
  
})

The models can now vary by outcome. Let’s see if we can improve our results.

matchRes <- do.call(rbind, lapply(1:length(m), function(model){
  val <- as.data.frame(cbind(
    standard.diff=m[[model]][[2]]$AfterMatching[[1]]$sdiff, 
    var.ratio = m[[model]][[2]]$AfterMatching[[1]]$var.ratio,
    sdiff.adj = m[[model]][[2]]$AfterMatching[[1]]$sdiff/100))
  return(val)
}))
namesInput <- NULL
for(i in 1:length(psmList)){
  namesInput[i] <- psmList[[i]]$y
}
rownames(matchRes) <- namesInput

print(kable(matchRes))

	standard.diff	var.ratio	sdiff.adj
m3.Ca	-6.4794848	0.8941595	-0.0647948
m3.Mg	-2.5858964	0.9904604	-0.0258590
pH	-5.2477889	1.0041375	-0.0524779
Total.N	-1.1367538	1.0242614	-0.0113675
Total.C	-0.6494876	1.0308477	-0.0064949
ExAl	4.7152699	1.0254513	0.0471527
n_season_fert	79.2241541	3.4210382	0.7922415
n_season_compost	8.4743566	0.9611436	0.0847436
n_seasons_leg_1	8.0648655	0.8951985	0.0806487
n_season_fallow	5.5545131	1.2544335	0.0555451
logFert	103.4470614	1.7397363	1.0344706
n_seasons_leg_2	-13.4710688	0.9138884	-0.1347107
n_season_lime	13.9533769	1.5456851	0.1395338
fert1.are	8.3515976	2.2500787	0.0835160
compost.are	7.8808248	1.0029775	0.0788082

Interpretation If I divide the standardized mean differences by 100, we meet the balance criteria of the standardized mean difference being close to 0 and the variance being close to 1. Let’s print out the model results to see how Tubura and control farmers compare on key soil meterics. These results should supercede the naive balance tables presented above.

We achieve acceptable balance for the soil attributes but we don’t for seasons of fertilizer use. This is isn’t entirely unexpected given that Tubura’s primary service is providing fertilizer inputs and training.

coefTable <- do.call(rbind, lapply(1:length(m), function(model){
  beta = round(m[[model]][[1]]$est.noadj,3)
  mean.Tr = round(m[[model]][[2]]$AfterMatching[[1]][[3]], 2)
  mean.Co = round(m[[model]][[2]]$AfterMatching[[1]][[4]], 2)
  pval = m[[model]][[2]]$AfterMatching[[1]][[10]][[3]] # p.value
  #pval = (1 - pnorm(abs(m[[model]][[1]]$est/m[[model]][[1]]$se.standard))) * 2
  pval = ifelse(pval < 0.001, "0.001", round(pval, 3))
  
  res = data.frame(beta, mean.Tr, mean.Co, pval)
  return(res)
}))
row.names(coefTable) <- namesInput
coefTable$pval.adj <- round(p.adjust(coefTable$pval, method="fdr"),3)

print(kable(coefTable))

	beta	mean.Tr	mean.Co	pval	pval.adj
m3.Ca	-40.669	796.70	837.37	0.052	0.097
m3.Mg	-3.230	208.99	212.22	0.421	0.486
pH	-0.032	5.46	5.49	0.097	0.146
Total.N	0.000	0.15	0.15	0.725	0.777
Total.C	-0.003	2.08	2.08	0.841	0.841
ExAl	0.028	0.61	0.58	0.144	0.196
n_season_fert	2.564	3.41	0.85	0.001	0.004
n_season_compost	0.318	5.93	5.61	0.009	0.027
n_seasons_leg_1	0.214	2.29	2.07	0.014	0.035
n_season_fallow	0.095	0.69	0.59	0.069	0.115
logFert	0.825	1.19	0.36	0.001	0.004
n_seasons_leg_2	-0.427	2.61	3.04	0.001	0.004
n_season_lime	0.097	0.23	0.13	0.001	0.004
fert1.are	0.172	1.22	1.05	0.312	0.390
compost.are	7.938	52.36	44.43	0.018	0.039

I’m comparing season 1 clients to clients with more tenure in the PSM matching but I also need this variable for the threshold calculations

# 0 is new client, 1 is returning client
d$tenure.tiers <- ifelse(d$client==1 & d$total.seasons==1, 0,
                         ifelse(d$client==1 & d$total.seasons>1, 1, NA))

5.1 Calculate farmers under soil health thresholds - table 1

thresh <- d %>% group_by(client) %>% dplyr::summarize(
  count = n(),
  ph = sum(pH<5.8),
  carbon = sum(Total.C < 2),
  nitrogen = sum(Total.N < 0.1),
  calcium = sum(m3.Ca < 1056),
  magnesium = sum(m3.Mg < 148)
  #aluminum = sum(ExAl)
) %>% mutate(
  under.ph = paste(paste(round(ph/count,4)*100, "%", sep=""), " (", ph, ")", sep=""),
  under.carbon = paste(paste(round(carbon/count,4)*100,"%", sep=""), " (", carbon, ")", sep=""),
  under.nitrogen = paste(paste(round(nitrogen/count,4)*100, "%", sep=""), " (", nitrogen, ")", sep=""),
  under.calcium = paste(paste(round(calcium/count,4)*100, "%", sep=""), " (", calcium, ")", sep=""),
  under.mag = paste(paste(round(magnesium/count,4)*100,"%", sep=""), " (", magnesium, ")", sep="")
) %>% as.data.frame() 
thresh <- thresh[, c("client", names(thresh)[grep("under", names(thresh))])]
thresh <- t(thresh)
colnames(thresh) = thresh[1, ] # the first row will be the header
colnames(thresh) = c("non-client", "client")
thresh = thresh[-1, ]
write.csv(thresh, file=paste("output", "table1_rw_thresholds.csv", sep = "/"), row.names = T)

New clients vs. returning clients summaries:

newOld <- d %>% filter(!is.na(tenure.tiers)) %>% group_by(tenure.tiers) %>% 
  summarise(
  ph = round(mean(pH, na.rm=T),3),
  carbon = round(mean(Total.C, na.rm=T),3),
  nitrogen = round(mean(Total.N, na.rm=T),3),
  calcium = round(mean(m3.Ca, na.rm=T),3),
  magnesium = round(mean(m3.Mg, na.rm=T),3)
) %>% ungroup() 
newOld <- as.data.frame(t(newOld))
colnames(newOld) = newOld[1, ] # the first row will be the header
colnames(newOld) = c("New Client", "Returning Client")
newOld = newOld[-1, ]
#write.csv(newOld, file=paste(od, "rw table1 new old values.csv", sep = "/"), row.names = T)

New Clients vs. returning clients thresholds

thresh.t <- d %>% filter(!is.na(tenure.tiers)) %>%
  group_by(tenure.tiers) %>% dplyr::summarize(
  count = n(),
  ph = sum(pH<5.8),
  carbon = sum(Total.C < 2),
  nitrogen = sum(Total.N < 0.1),
  calcium = sum(m3.Ca < 1056),
  magnesium = sum(m3.Mg < 148)
  #aluminum = sum(ExAl)
) %>% mutate(
  under.ph = paste(paste(round(ph/count,4)*100, "%", sep=""), " (", ph, ")", sep=""),
  under.carbon = paste(paste(round(carbon/count,4)*100,"%", sep=""), " (", carbon, ")", sep=""),
  under.nitrogen = paste(paste(round(nitrogen/count,4)*100, "%", sep=""), " (", nitrogen, ")", sep=""),
  under.calcium = paste(paste(round(calcium/count,4)*100, "%", sep=""), " (", calcium, ")", sep=""),
  under.mag = paste(paste(round(magnesium/count,4)*100,"%", sep=""), " (", magnesium, ")", sep="")
) %>% as.data.frame() 
thresh.t <- thresh.t[, c("tenure.tiers", names(thresh.t)[grep("under", names(thresh.t))] )]
thresh.t <- t(thresh.t)
colnames(thresh.t) = thresh.t[1, ] # the first row will be the header
colnames(thresh.t) = c("new client thold", "returning client thold")
thresh.t = thresh.t[-1, ]
# combine newOld summaries and percents below percentiles
thresh.t <- cbind(newOld, thresh.t)
write.csv(thresh.t, file=paste("output", "table1_rw_thresholds_tenure.csv", sep = "/"), row.names = T)

5.2 Table 1:3 coefficients

#
write.csv(coefTable, file=paste("output", "psm coefficients.csv", sep = "/"),
          row.names = T)
# sort by the order Eric wants
t1order <- c("pH", "Total.C", "Total.N", "m3.Ca", "m3.Mg", "ExAl")
table1vars <- paste(t1order, collapse = "|")
table1 <- coefTable[grep(table1vars, rownames(coefTable)), ]
table1 <- table1[order(match(rownames(table1), t1order)),]
write.csv(table1, file=paste("output", "psm coefficients ordered for ES.csv", sep = "/"))
# 11/17 added lime
t2order <- c("n_season_fert", "n_season_compost", "n_season_lime", "n_season_fallow", "n_seasons_leg_1", "n_seasons_leg_2")
table2vars <- paste(t2order, collapse = "|")
table2 <- coefTable[grep(table2vars, rownames(coefTable)), ]
table2 <- table2[order(match(rownames(table2), t2order)),]
write.csv(table2, file=paste("output", "psm coefficients ordered for ES_agprac.csv", sep = "/"))
#table 3
t3order <- c("fert1.are", "compost.are")
table3vars <- paste(t3order, collapse = "|")
table3 <- coefTable[grep(table3vars, rownames(coefTable)), ]
table3 <- table3[order(match(rownames(table3), t3order)),]
write.csv(table3, file=paste("output", "psm coefficients table3.csv", sep = "/"))

Interpretation: Propensity score matching gives us a comparable treatment and control group. The table above shows that after matching on those characteristics, there are effectively no differences between One Acre Fund farmer and Tubura farmers on soil attributes. The unadjusted p-values show 1AF farmers to have slow levels of soil nitrogen but this finding disappears if we account for running multiple matching models.

When we expand the outcome variable set to include practice variables, we first no longer get a good propensity score match for all variables.

• Tubura participation so strongly overlaps with fertilizer use that the comparison group is no longer comparable. The resulting model shows a significant difference in seasons of fertilizer use.
• Tubura farmers have used compost for 0.3 more seasons than non-Tubura farmers. We don’t observe a difference in N or C levels in the soil attributes however.
• We don’t see a difference between Tubura and non-Tubura farmers on seasons of legumes being the primary crop in the study plot.

5.3 Break out first season clients from more tenured - for appendix?

This requires us to re-run the PSM matching process, confirm that our models are sound and then output the results ala table 2 again.

psmList <- list(
  list(tr = "tenure.tiers",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="m3.Ca"),
  list(tr = "tenure.tiers",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="m3.Mg"),
  list(tr = "tenure.tiers",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="pH"),
  list(tr = "tenure.tiers",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="Total.N"),
  list(tr = "tenure.tiers",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="Total.C"),
  list(tr = "tenure.tiers",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="ExAl"),
  list(tr = "tenure.tiers",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="n_season_fert"),
  list(tr = "tenure.tiers",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="n_season_compost"),
  list(tr = "tenure.tiers",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="n_seasons_leg_1"),
  list(tr = "tenure.tiers",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="n_season_fallow"),
  list(tr = "tenure.tiers",
       psmVars = paste(coreVars,
                   collapse=" + "),
       y="logFert"),
  list(tr = "tenure.tiers",
       psmVars = paste(c(coreVars, "age2",
                   "hhsize2"),
                   collapse=" + "),
       y="n_seasons_leg_2"),
  list(tr = "tenure.tiers",
       psmVars = paste(c(coreVars, "age2",
                   "hhsize2"),
                   collapse=" + "),
       y="n_season_lime"),
  list(tr = "tenure.tiers",
       psmVars = paste(c("female", "age", "hhsize", "own",
                         "cows", "goats", "chickens", "pigs", "sheep",
                         "as.factor(district)", "age2", "hhsize2",
                         "hhsize_age"),
                   collapse=" + "),
       y="fert1.are"),
  # list(tr = "tenure.tiers",
  #      psmVars = paste(c("female", "age", "hhsize","as.factor(district)",
  #                  "cows", "goats", "chickens", "pigs", "sheep","age2", "hhsize2",
  #                        "hhsize_age"),
  #                  collapse=" + "),
  #      y="fert2.are"),
  list(tr = "tenure.tiers",
       psmVars = paste(c("female", "age", "hhsize","as.factor(district)",
                   "cows", "goats", "chickens", "pigs", "sheep","age2", "hhsize2",
                         "hhsize_age"),
                   collapse=" + "),
       y="compost.are")
  
)
# fertilizer application in 15B has missing values at the cell level so i include
# a district level control instead. We don't get a good fit with the current model.
# adjust!
# aggregate(d$fert1.are, by=list(d$client, d$cell), FUN=mean, na.rm=T)
# lime.are is also being finicky
# PSM
set.seed(20161102)
mappend <- lapply(psmList, function(listInput){
  naCheck <- unlist(strsplit(gsub("\\+", "", as.vector(listInput$psmVars))," ", fixed=TRUE))
  naCheck <- naCheck[-which(naCheck=="")]
  
  # keep complete cases of outcome variable
  k <- d[complete.cases(d[,listInput$y]),]
  k <- k[complete.cases(k[,listInput$y]),]
  k <- k[complete.cases(k[,listInput$tr]),]
  
  # run glm regression:
  reg <- glm(as.formula(paste(listInput$tr, "~", listInput$psmVars, sep="")),  family= binomial(link="logit"), data=k)
  
  suppressWarnings(
  mod <- Match(Y = k[,listInput$y], Tr = k[,listInput$tr], X = reg$fitted, ties=FALSE, replace=FALSE, caliper=0.25, estimand = "ATE")   
  )
  matchRes <- MatchBalance(k[,listInput$tr] ~ k[,listInput$y], match.out=mod, nboots=500, data=k, print.level = 0)
  #print(listInput$y)
  return(list(mod, matchRes))
  
})
matchRes <- do.call(rbind, lapply(1:length(mappend), function(model){
  val <- as.data.frame(cbind(
    standard.diff=m[[model]][[2]]$AfterMatching[[1]]$sdiff, 
    var.ratio = m[[model]][[2]]$AfterMatching[[1]]$var.ratio,
    sdiff.adj = m[[model]][[2]]$AfterMatching[[1]]$sdiff/100))
  return(val)
}))
namesInput <- NULL
for(i in 1:length(psmList)){
  namesInput[i] <- psmList[[i]]$y
}
rownames(matchRes) <- namesInput

print(kable(matchRes))

	standard.diff	var.ratio	sdiff.adj
m3.Ca	-6.4794848	0.8941595	-0.0647948
m3.Mg	-2.5858964	0.9904604	-0.0258590
pH	-5.2477889	1.0041375	-0.0524779
Total.N	-1.1367538	1.0242614	-0.0113675
Total.C	-0.6494876	1.0308477	-0.0064949
ExAl	4.7152699	1.0254513	0.0471527
n_season_fert	79.2241541	3.4210382	0.7922415
n_season_compost	8.4743566	0.9611436	0.0847436
n_seasons_leg_1	8.0648655	0.8951985	0.0806487
n_season_fallow	5.5545131	1.2544335	0.0555451
logFert	103.4470614	1.7397363	1.0344706
n_seasons_leg_2	-13.4710688	0.9138884	-0.1347107
n_season_lime	13.9533769	1.5456851	0.1395338
fert1.are	8.3515976	2.2500787	0.0835160
compost.are	7.8808248	1.0029775	0.0788082

coefTable.append <- do.call(rbind, lapply(1:length(mappend), function(model){
  beta = round(mappend[[model]][[1]]$est.noadj,3)
  mean.Tr = round(mappend[[model]][[2]]$AfterMatching[[1]][[3]], 2)
  mean.Co = round(mappend[[model]][[2]]$AfterMatching[[1]][[4]], 2)
  pval = mappend[[model]][[2]]$AfterMatching[[1]][[10]][[3]] # p.value
  #pval = (1 - pnorm(abs(m[[model]][[1]]$est/m[[model]][[1]]$se.standard))) * 2
  pval = ifelse(pval < 0.001, "0.001", round(pval, 3))
  
  res = data.frame(beta, mean.Tr, mean.Co, pval)
  return(res)
}))
row.names(coefTable.append) <- namesInput
coefTable.append$pval.adj <- round(p.adjust(coefTable.append$pval, method="fdr"),3)
names(coefTable.append) <- c("beta", "Old Clients", "New Clients", "pval", "pval.adj")

print(kable(coefTable.append))

	beta	Old Clients	New Clients	pval	pval.adj
m3.Ca	-46.924	824.09	871.02	0.425	0.708
m3.Mg	-3.113	208.98	212.09	0.79	0.846
pH	-0.005	5.49	5.49	0.924	0.924
Total.N	-0.007	0.15	0.16	0.021	0.105
Total.C	-0.071	2.04	2.11	0.089	0.270
ExAl	0.028	0.61	0.58	0.605	0.846
n_season_fert	1.640	3.55	1.91	0.001	0.008
n_season_compost	0.560	5.80	5.24	0.09	0.270
n_seasons_leg_1	0.213	2.28	2.07	0.389	0.708
n_season_fallow	0.044	0.63	0.59	0.739	0.846
logFert	0.473	1.31	0.83	0.001	0.008
n_seasons_leg_2	0.102	2.36	2.26	0.702	0.846
n_season_lime	0.050	0.23	0.18	0.305	0.654
fert1.are	0.153	1.35	1.20	0.715	0.846
compost.are	7.693	48.77	41.08	0.243	0.607

# sort by the order Eric wants
t1order.a <- c("pH", "Total.C", "Total.N", "m3.Ca", "m3.Mg", "ExAl")
table1vars.a <- paste(t1order.a, collapse = "|")
table1.a <- coefTable.append[grep(table1vars.a, rownames(coefTable.append)), ]
table1.a <- table1.a[order(match(rownames(table1.a), t1order.a)),]
write.csv(table1.a, file=paste("output", "psm coefficients ordered for ES-appendix.csv", sep = "/"))
# 11/17 added lime
t2order.a <- c("n_season_fert", "n_season_compost", "n_season_lime", "n_season_fallow", "n_seasons_leg_1", "n_seasons_leg_2")
table2vars.a <- paste(t2order.a, collapse = "|")
table2.a <- coefTable.append[grep(table2vars.a, rownames(coefTable.append)), ]
table2.a <- table2.a[order(match(rownames(table2.a), t2order.a)),]
write.csv(table2.a, file=paste("output", "psm coefficients ordered for ES_agprac-appendix.csv", sep = "/"))
#table 3
t3order.a <- c("fert1.are", "compost.are")
table3vars.a <- paste(t3order.a, collapse = "|")
table3.a <- coefTable.append[grep(table3vars.a, rownames(coefTable.append)), ]
table3.a <- table3.a[order(match(rownames(table3.a), t3order.a)),]
write.csv(table3.a, file=paste("output", "psm coefficients table3-appendix.csv", sep = "/"))

5.4 Study group balance post-match

See here for some guidance on hwo to use weights to reconstruct the group balance following the matches.

See here for weighted t.test documentation

suppressMessages(library(weights))
tableVars <- c("age", "female", "hhsize", "own")
postMatch <- do.call(rbind, lapply(1:length(m), function(model){
  
  innerPost <- do.call(rbind, lapply(tableVars, function(x){
  
  mean.t = weighted.mean(d[m[[model]][[1]]$index.treated,][,x], m[[model]][[1]]$weights)
  mean.c = weighted.mean(d[m[[model]][[1]]$index.control,][,x], m[[model]][[1]]$weights)
  
  # combined data
  dm <- as.data.frame(rbind(d[m[[model]][[1]]$index.treated,], 
              d[m[[model]][[1]]$index.control,]))
  
  test = wtd.t.test(d[m[[model]][[1]]$index.treated,][,x], 
                    d[m[[model]][[1]]$index.control,][,x],
                    weight=m[[model]][[1]]$weights,
                    samedata=TRUE)
  
  return(data.frame(model.num = model, 
                    outcome=x, 
                    tr=mean.t, 
                    contr = mean.c,
                    pval = test$coefficients[3][[1]]))
  }))
  
  return(innerPost)
}))

write.csv(postMatch, file=paste("output", "rw post match covars.csv", sep = "/"),
          row.names = F)

5.5 Models with PSM matched farmers

Per Robert’s suggestion, now that we’ve matched Tubura and non-Tubura farmers, let’s assess the severity of Tubura tenure on key soil health outcomes.

tenureTab.add <- lapply(1:length(m), function(model){
  
  dm <- as.data.frame(rbind(d[m[[model]][[1]]$index.treated,], 
              d[m[[model]][[1]]$index.control,]))
  dm$client_tenure <- dm$client*dm$total.seasons
  mod <- lm(as.formula(paste("dm[,psmList[[model]]$y] ~", "total.seasons + as.factor(cell)", sep ="")), data=dm)
  return(mod)
})
# add tenured>=3 to d if we want to run this as a binary comparison
# tenureTab.binary <- lapply(1:length(m), function(model){
#   
#   dm <- as.data.frame(rbind(d[m[[model]][[1]]$index.treated,], 
#               d[m[[model]][[1]]$index.control,]))
#   dm$client_tenure <- dm$client*dm$total.seasons
#   mod <- lm(as.formula(paste("dm[,psmList[[model]]$y] ~", "tenured + as.factor(cell)", sep ="")), data=dm)
#   return(mod)
# })

# modNames <- c("Calcium", "Magnesium", "pH", "Nitrogen", "Carbon", "Seasons fertilizer", "Seasons compost", "Seasons legumes", "Seasons fallow", "Fertilizer (log)", "Season Sec. legumes", "Seasons Lime", "Fertilizer/Are", "Compost/are")
modNames <- unlist(lapply(psmList, function(x){
  return(x$y)
}))

suppressWarnings(
stargazer(tenureTab.add, type="html", 
          title = "2016A Rwanda Soil Health Baseline - PSM Tenure",
          covariate.labels = "One Acre Fund Tenure", 
          dep.var.labels = "",
          #column.labels = modNames,
          #column.labels = c(gsub("m3.", "", as.vector(namesInput))),
          notes = "Includes FE for cell",
          omit=c("cell"), out=paste(od, "rw_baseline_matched_tenure.htm", sep="/"))
)

plm.tenure <- function(x){
  
  intercept = x$coefficients[[1]]
  beta = round(x$coefficients[[2]],3)
  int.pval = summary(x)$coefficients[1,4]
  int.pval = ifelse(int.pval < 0.001, "< 0.001", round(as.numeric(int.pval),3))
  beta.pval = summary(x)$coefficients[2,4]
  beta.pval = ifelse(beta.pval < 0.001, "< 0.001", round(as.numeric(beta.pval),3))
  res = data.frame(intercept, int.pval, beta, beta.pval, stringsAsFactors = F)
  
  return(res)  
}
tenure.reg <- do.call(rbind, lapply(tenureTab.add, function(x){
  plm.tenure(x)
}))
rownames(tenure.reg) <- modNames

t6order <- c("cows", "pigs", "sheep", "goats", "chickens")
table6vars <- paste(t6order, collapse = "|")
rw.table6 <- output[grep(table6vars, rownames(output)), ]
rw.table6 <- rw.table6[order(match(rownames(rw.table6), t4order)),]
ESorder <- c("pH", "Total.C", "Total.N", "m3.Ca", "m3.Mg", "ExAl", "n_season_fert",
             "logFert", "n_season_compost", "n_seasons_leg_1", "n_season_fallow")
ESorder.grep <- paste(ESorder, collapse = "|")
tenure.reg <- tenure.reg[grep(ESorder.grep,rownames(tenure.reg)),]
tenure.reg <- tenure.reg[order(match(row.names(tenure.reg), ESorder)),]
write.csv(tenure.reg, file=paste(od, "table13_reg.csv", sep = "/"), row.names=T)

Interpretation: Using a PSM matched sample, the models above assess the effects of additional years of farming with Tubura. Numerous control farmers have also been Tubura farmers in previous seasons. Thus, I’m keeping the model simple instead of adding a client*tenure interaction. We can easily test that as well though.

6 Soil Clusters

6.1 Heirarchical Clustering

I draw from Robert’s example in the BGMS soil response notebook.

Scale soil variables to remove units as an issue.

scaledSoil <- scale(d[,soilVars])

clustMethods <- c("ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid")
invisible(sapply(clustMethods, FUN=function(method) {
    plot(hclust(dist(scaledSoil), method=method), main=method, ylab="", label=FALSE)
}))

Note: I’m honestly not certain what we’re looking for in these outputs. Look for a balanced tree? Ward looks pretty balanced.

hc <- hclust(dist(scaledSoil), method="ward.D2")

d$hc4 <- cutree(hc, k=4)
hcCentroids <- aggregate(list(d[,soilVars]), by=list(cluster=d$hc4), FUN=mean)
hcTable <- rbind(t(table(d$hc4)),
      t(round(hcCentroids, 2)))

print(kable(hcTable))

	1	2	3	4
	726.00	806.00	503.00	404.00
cluster	1.00	2.00	3.00	4.00
m3.Ca	709.60	329.10	910.03	1893.42
m3.Mg	210.68	111.16	236.64	371.69
pH	5.63	4.83	5.63	6.33
Total.N	0.12	0.16	0.17	0.17
Total.C	1.65	2.32	2.20	2.23
ExAl	0.24	1.30	0.33	0.12

6.2 K-means clustering

K-means clustering is a typical unsupervised learning algorithm. We start by identifying the number of clusters we should tell the formula to look for.

numClusters <- 2:10
plot(numClusters,
     sapply(numClusters, FUN=function(k) { sum(kmeans(scaledSoil, centers=k, nstart=20, iter.max=20)$withinss) }),
     type="b", ylab="average within cluster sum of squared error")

We’re looking for the bend in the graph. It seems to come at 3 but we can try both 3 and 4 and see how they match with our understanding of AEZ and environmental conditions.

set.seed(20161125)
clusters <- 4
d$km4 <- kmeans(scaledSoil, centers=clusters, nstart=20, iter.max=20)$cluster
kmCentroids <- aggregate(list(d[,soilVars]), by=list(cluster=d$km4), FUN=mean)
kmTable <- rbind(t(table(d$km4)),
      t(round(kmCentroids, 2)))

print(kable(kmTable))

	1	2	3	4
	462.00	669.00	972.00	336.00
cluster	1.00	2.00	3.00	4.00
m3.Ca	1761.59	385.48	846.72	322.50
m3.Mg	373.34	130.16	224.14	102.13
pH	6.17	4.97	5.75	4.76
Total.N	0.19	0.14	0.13	0.19
Total.C	2.38	2.01	1.75	2.76
ExAl	0.19	0.94	0.20	1.56

These clusters are roughly equally sized. Robert used another method, partitioning around Mediods, to better account for outliers that were causing the algorithm to create a small additional cluster.

Give the clusters brief descriptions

d$km4 <- factor(d$km4, labels=c("pH=5.9, C=1.7", 
                                "pH=6.1,C=2.4", 
                                "pH=5.1, C=2",
                                "pH=4.8, C=2.6"))

6.3 Mappaing the location of the clusters

suppressMessages(library(ggmap))
qmplot(lon, lat, data=d, color=d$km4)

Using zoom = 9...
Source : http://tile.stamen.com/toner-lite/9/297/258.png
Source : http://tile.stamen.com/toner-lite/9/298/258.png
Source : http://tile.stamen.com/toner-lite/9/299/258.png
Source : http://tile.stamen.com/toner-lite/9/297/259.png
Source : http://tile.stamen.com/toner-lite/9/298/259.png
Source : http://tile.stamen.com/toner-lite/9/299/259.png
Source : http://tile.stamen.com/toner-lite/9/297/260.png
Source : http://tile.stamen.com/toner-lite/9/298/260.png
Source : http://tile.stamen.com/toner-lite/9/299/260.png

#qmplot(lon, lat, data=d, color=as.factor(hc4))

crdref <- CRS('+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0')
e <- d[!is.na(d$lon),]
ss <- SpatialPointsDataFrame(coords = e[, c("lon", "lat")], data=e, proj4string = crdref)
pal <- colorFactor("RdYlBu", domain=unique(ss$km4))
leaflet() %>% addTiles() %>%
  setView(lng=rwanda$longitude, lat=rwanda$latitude, zoom=8) %>%
  addCircleMarkers(lng=ss$lon, lat=ss$lat, 
                   #radius= ifelse(ss$km4==1, 10,6),
                   color = pal(ss$km4), clusterOptions = markerClusterOptions(disableClusteringAtZoom=11, spiderfyOnMaxZoom=FALSE)) %>% 
  addLegend(pal = pal, values=unique(ss$km4), title = "Clusters ")

NA

6.4 Soil clusters by altitude and AEZ

ggplot(d, aes(x=km4, y=alt)) + geom_boxplot()

6.4.1 Soil clusters by altitude and rainfall

Try this when we have aWhere data

6.5 Compare clusters to AEZ

print(kable(table(d$km4, d$aez)))

	Central Plateau	Congo Nile	East	Lake Kivu
pH=5.9, C=1.7	65	11	291	95
pH=6.1,C=2.4	154	268	57	190
pH=5.1, C=2	345	82	253	292
pH=4.8, C=2.6	30	235	13	58

We’re not seeing close alignment between clusters and AEZ. This is probably because AEZ are a mix of altitude, rainfall, temperature and soil. When we have weather data we can try to cluster again and see if we’re more closely aligned.

Alternatively, this could be initial evidence that the AEZ designations are not capturing and separating variation very well. I think we need to start with more data though.

7 Mapping

7.1 Soil health maps for soil health study areas

#crdref <- CRS('+proj=longlat +datum=WGS84')
crdref <- CRS('+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0')
e <- d[!is.na(d$lon),]
ss <- SpatialPointsDataFrame(coords = e[, c("lon", "lat")], data=e, proj4string = crdref)
rw <- getData("GADM", country='RW', level=3, path = "/Users/mlowes/drive/optimized_agronomy/soil/soil_health_study/data") # the higher the number, the higher the resolution
#ext.rw.ss matches points with spatial polygons in rw
ext.rw.ss <- extract(rw[, "NAME_3"], ss)

Loading required namespace: rgeos

ss$spatialname <- ext.rw.ss$NAME_3
frw <- fortify(rw, region="NAME_3")
#for soil features
ss.soil <- aggregate(ss@data[,soilVars], by=list(ss@data$spatialname), function(x){
  mean(x, na.rm=T)
})
plotReady <- dplyr::left_join(frw, ss.soil, by=c("id"="Group.1"))
ss.count <- ss@data %>% group_by(spatialname) %>% dplyr::summarize(
  count = n()
) %>% as.data.frame()
countPlot <- dplyr::left_join(frw, ss.count, by=c("id"="spatialname"))

Generate district density summaries for Nathaniel to pair with the map

districtDensity <- d %>% group_by(district) %>% dplyr::summarize(
  count = n()
) %>% as.data.frame()
cellDensity <- d %>% group_by(district, cell) %>% dplyr::summarize(
  count = n()
) %>% as.data.frame()
write.csv(districtDensity, file=paste("output", "rw district density table.csv", sep = "/"),
          row.names=T)
write.csv(cellDensity, file=paste("output", "rw cell density table.csv", sep = "/"),
          row.names=T)

7.2 Static map of sampling density

library(RColorBrewer)
mapRes <-  ggplot(countPlot, aes(x=long, y=lat, group=group)) + geom_path() + 
  geom_polygon(aes(fill=countPlot$count)) + 
  #coord_map(xlim = c(33.5, 36),ylim = c(-2, 1.75)) +
  scale_fill_gradientn(colours = brewer.pal(9,"Reds"), # define colors
        name = "Sampling Density",
        guide = guide_colorbar(legend.direction = "vertical")) + 
  theme_bw() + 
  labs(title="Rwanda soil health study sampling density", x = "Longitude", y="Latitude")
print(mapRes)
pdf(paste(od, "rwanda sampling density.pdf", sep = "/"), width=11, height=8.5)

print(mapRes)
dev.off()

quartz_off_screen 
                2 

7.3 Plot simple summary of soil characteristics

Consider revising these maps to a smaller greographic unit. Add the name of the location for uninitiated users.

library(RColorBrewer)
mapList <- list()
for(i in 1:length(soilVars)){
mapRes <-  ggplot(plotReady, aes(x=long, y=lat, group=group)) + geom_path() + 
  geom_polygon(aes(fill=plotReady[,soilVars[i]])) + 
  scale_fill_gradientn(colours = rev(brewer.pal(9,"Reds")), # define colors
                       name = soilVars[i],
                       guide = guide_colorbar(legend.direction = "vertical")) + 
  theme_bw() + 
  labs(title=paste("Rwanda long term soil health baseline - 2016", soilVars[i], sep= " "), x = "Longitude", y="Latitude")
mapList[[i]] <- mapRes
print(mapRes)
} 

# This is a small experiment to combine raster (spdf) and leaflet and be able to access the data in the raster interactively.
#mapLayer <- sp::merge(rw, ss.soil, by.x="NAME_3", by.y="Group.1")

# fill = T, fillOpacity = 0.7, fillColor = d.fill, 
#         stroke = T, color = "white", weight = 2, dashArray = 3, 
#         opacity = 0.5, popup = county.tt(d)

# leaflet(mapLayer) %>% addTiles() %>%
#   setView(lng=rwanda$longitude, lat=rwanda$latitude, zoom=8) %>%
#   addPolygons(
#     stroke = TRUE, opacity=0.2, smoothFactor = 0.5, 
#     fillColor=mapLayer$pH, fillOpacity = 0.5)

pdf(file=paste(md, "rw_shs_baseline_soil_maps.pdf", sep = "/"), width=11, height=8.5)

Error in paste(md, "rw_shs_baseline_soil_maps.pdf", sep = "/") : 
  object 'md' not found

7.4 Interpolations of soil health values

Interpolate soil health values for full operating area using soil health study values. We want to eventually add all Rwandan soil values into a single dataset to update and hone these values. See here for more guidanace

note:

• Layer on the Tubura sites to the interpolated map.
• Use leaflet so you can zoom in and out more easily.
• Make it a raster layer that you can click on understand the values at different locations and the name of the location. I’ll need to make this a shiny app to have that functionality

The code below will run 5 K-fold cross validation to compare interpolation models. The output will be fed into the interpolate leaflet code below.

Check that I’m handling the projection correctly with Robert

Now loop over the variables of interest where x is the soilVar variable.

7.5 Interpolated soil maps

Print out the interpolated values for inclusion in the report.

pdf(file=paste(md, "rw_shs_bl_interpolation_soil_vars.pdf", sep = '/'), width=11, height=8.5)
lapply(soilVars, function(x) {
  m <- Tps(coordinates(ss), ss@data[,x])
  # make raster layer with model, raster is rwanda empty raster, model is m
  tps <- interpolate(r, m)
  tps <- crop(tps, rw)
  tps <- raster::mask(tps, rw) # cuts the tps raster down to the rw boundaries
  x <- gsub("m3.", "", x)
  
suppressMessages(
  outPlot <- invisible(print(
  plot(tps, main= paste("Soil TPS Interpolation ", x, sep="- ")),
  plot(rw, add=T, na.print=NULL)
  ))
)
})

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dev.off()

null device 
          1 

–end